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Sample records for neural adaptations potential

  1. Adaptive quantization of local field potentials for wireless implants in freely moving animals: an open-source neural recording device

    Science.gov (United States)

    Martinez, Dominique; Clément, Maxime; Messaoudi, Belkacem; Gervasoni, Damien; Litaudon, Philippe; Buonviso, Nathalie

    2018-04-01

    Objective. Modern neuroscience research requires electrophysiological recording of local field potentials (LFPs) in moving animals. Wireless transmission has the advantage of removing the wires between the animal and the recording equipment but is hampered by the large number of data to be sent at a relatively high rate. Approach. To reduce transmission bandwidth, we propose an encoder/decoder scheme based on adaptive non-uniform quantization. Our algorithm uses the current transmitted codeword to adapt the quantization intervals to changing statistics in LFP signals. It is thus backward adaptive and does not require the sending of side information. The computational complexity is low and similar at the encoder and decoder sides. These features allow for real-time signal recovery and facilitate hardware implementation with low-cost commercial microcontrollers. Main results. As proof-of-concept, we developed an open-source neural recording device called NeRD. The NeRD prototype digitally transmits eight channels encoded at 10 kHz with 2 bits per sample. It occupies a volume of 2  ×  2  ×  2 cm3 and weighs 8 g with a small battery allowing for 2 h 40 min of autonomy. The power dissipation is 59.4 mW for a communication range of 8 m and transmission losses below 0.1%. The small weight and low power consumption offer the possibility of mounting the entire device on the head of a rodent without resorting to a separate head-stage and battery backpack. The NeRD prototype is validated in recording LFPs in freely moving rats at 2 bits per sample while maintaining an acceptable signal-to-noise ratio (>30 dB) over a range of noisy channels. Significance. Adaptive quantization in neural implants allows for lower transmission bandwidths while retaining high signal fidelity and preserving fundamental frequencies in LFPs.

  2. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  3. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  4. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  5. Direct adaptive control using feedforward neural networks

    OpenAIRE

    Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

    2003-01-01

    ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

  6. Dysfunction of Rapid Neural Adaptation in Dyslexia.

    Science.gov (United States)

    Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

    2016-12-21

    Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  8. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  9. Emotional facial expressions reduce neural adaptation to face identity.

    Science.gov (United States)

    Gerlicher, Anna M V; van Loon, Anouk M; Scholte, H Steven; Lamme, Victor A F; van der Leij, Andries R

    2014-05-01

    In human social interactions, facial emotional expressions are a crucial source of information. Repeatedly presented information typically leads to an adaptation of neural responses. However, processing seems sustained with emotional facial expressions. Therefore, we tested whether sustained processing of emotional expressions, especially threat-related expressions, would attenuate neural adaptation. Neutral and emotional expressions (happy, mixed and fearful) of same and different identity were presented at 3 Hz. We used electroencephalography to record the evoked steady-state visual potentials (ssVEP) and tested to what extent the ssVEP amplitude adapts to the same when compared with different face identities. We found adaptation to the identity of a neutral face. However, for emotional faces, adaptation was reduced, decreasing linearly with negative valence, with the least adaptation to fearful expressions. This short and straightforward method may prove to be a valuable new tool in the study of emotional processing.

  10. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  11. Neural Adaptation Effects in Conceptual Processing

    Directory of Open Access Journals (Sweden)

    Barbara F. M. Marino

    2015-07-01

    Full Text Available We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view.

  12. Intrinsic gain modulation and adaptive neural coding.

    Directory of Open Access Journals (Sweden)

    Sungho Hong

    2008-07-01

    Full Text Available In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.

  13. Adaptive Filtering Using Recurrent Neural Networks

    Science.gov (United States)

    Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  14. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

    Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

  15. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  16. Relationship between neural response and adaptation selectivity to form and color: an ERP study

    Directory of Open Access Journals (Sweden)

    Ilias eRentzeperis

    2012-04-01

    Full Text Available Adaptation is widely used as a tool for studying selectivity to visual features. In these studies it is usually assumed that the loci of feature selective neural responses and adaptation coincide. We used an adaptation paradigm to investigate the relationship between response and adaptation selectivity in event-related potentials (ERP. ERPs were evoked by the presentation of colored Glass patterns in a form discrimination task. Response selectivities to form and, to some extent, color of the patterns were reflected in the C1 and N1 ERP components. Adaptation selectivity to color was reflected in N1 and was followed by a late (300-500 ms after stimulus onset effect of form adaptation. Thus for form, response and adaptation selectivity were manifested in non-overlapping intervals. These results indicate that adaptation and response selectivity can be associated with different processes. Therefore inferring selectivity from an adaptation paradigm requires analysis of both adaptation and neural response data.

  17. Learning and adaptation: neural and behavioural mechanisms behind behaviour change

    Science.gov (United States)

    Lowe, Robert; Sandamirskaya, Yulia

    2018-01-01

    This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neural-dynamic modelling of infant development in the Piagetian A-not-B task. The aim of the special issue, through its contributions, is to highlight some of the critical neural-dynamic and behavioural aspects of learning as it grounds adaptive responses in robotic- and neural-dynamic systems.

  18. Nonlinear adaptive inverse control via the unified model neural network

    Science.gov (United States)

    Jeng, Jin-Tsong; Lee, Tsu-Tian

    1999-03-01

    In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

  19. Adaptive training of feedforward neural networks by Kalman filtering

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1995-02-01

    Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)

  20. Adaptive nonlinear control using input normalized neural networks

    International Nuclear Information System (INIS)

    Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong

    2008-01-01

    An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small

  1. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    OpenAIRE

    Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing

    2017-01-01

    This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) tech...

  2. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  3. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    International Nuclear Information System (INIS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-01-01

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

  4. Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors.

    Science.gov (United States)

    Yu, Jinpeng; Shi, Peng; Dong, Wenjie; Chen, Bing; Lin, Chong

    2015-03-01

    This brief considers the problem of neural networks (NNs)-based adaptive dynamic surface control (DSC) for permanent magnet synchronous motors (PMSMs) with parameter uncertainties and load torque disturbance. First, NNs are used to approximate the unknown and nonlinear functions of PMSM drive system and a novel adaptive DSC is constructed to avoid the explosion of complexity in the backstepping design. Next, under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced to only one, and the designed neural controllers structure is much simpler than some existing results in literature, which can guarantee that the tracking error converges to a small neighborhood of the origin. Then, simulations are given to illustrate the effectiveness and potential of the new design technique.

  5. Stability and Adaptation of Neural Networks

    Science.gov (United States)

    1990-11-02

    RICE CODE 17. SECURITY CLASSIFICATION 18. SECURI ’(CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF OF REPORT OF REP( RT OF REPORT...Recognition," Proc. European Conference on neural Netowrks , Prague, Czechoslovakia, September 1990. 3.0 NEXT-YEAR RESEARCH OBJECTIVES In the third

  6. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  7. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-01-01

    Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

  8. Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

    Science.gov (United States)

    Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

    2018-01-01

    Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural

  9. Adaptive Regularization of Neural Networks Using Conjugate Gradient

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    1998-01-01

    Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique........ Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost...

  10. Predictive Acoustic Tracking with an Adaptive Neural Mechanism

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    model of the lizard peripheral auditory system to extract information regarding sound direction. This information is utilised by a neural machinery to learn the acoustic signal’s velocity through fast and unsupervised correlation-based learning adapted from differential Hebbian learning. This approach...

  11. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    Energy Technology Data Exchange (ETDEWEB)

    Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  12. Adaptive neural network motion control for aircraft under uncertainty conditions

    Science.gov (United States)

    Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

    2018-02-01

    We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

  13. Adaptive control of a PWR core power using neural networks

    International Nuclear Information System (INIS)

    Arab-Alibeik, H.; Setayeshi, S.

    2005-01-01

    Reactor power control is important because of safety concerns and the call for regular and appropriate operation of nuclear power plants. It seems that the load-follow operation of these plants will be unavoidable in the future. Discrepancies between the real plant and the model used in controller design for load-follow operation encourage one to use auto-tuning and (or) adaptive techniques. Neural network technology shows great promise for addressing many problems in non-model-based adaptive control methods. Also, there has been a great attention to inverse control especially in the neural and fuzzy control context. Fortunately, online adaptation eliminates some limitations of inverse control and its shortcomings for real world applications. We use a neural adaptive inverse controller to control the power of a PWR reactor. The stability of the system and convergence of the controller parameters are guaranteed during online adaptation phase provided the controller is near the plant's real inverse after offline training period. The performance of the controller is verified using nonlinear simulations in diverse operating conditions

  14. Patterns of interval correlations in neural oscillators with adaptation

    Directory of Open Access Journals (Sweden)

    Tilo eSchwalger

    2013-11-01

    Full Text Available Neural firing is often subject to negative feedback by adaptationcurrents. These currents can induce strong correlations among the timeintervals between spikes. Here we study analytically the intervalcorrelations of a broad class of noisy neural oscillators withspike-triggered adaptation of arbitrary strength and time scale. Ourweak-noise theory provides a general relation between the correlationsand the phase-response curve (PRC of the oscillator, provesanti-correlations between neighboring intervals for adapting neuronswith type I PRC and identifies a single order parameter thatdetermines the qualitative pattern of correlations. Monotonicallydecaying or oscillating correlation structures can be related toqualitatively different voltage traces after spiking, which can beexplained by the phase plane geometry. At high firing rates, thelong-term variability of the spike train associated with thecumulative interval correlations becomes small, independent of modeldetails. Our results are verified by comparison with stochasticsimulations of the exponential, leaky, and generalizedintegrate-and-fire models with adaptation.

  15. Neural predictors of sensorimotor adaptation rate and savings.

    Science.gov (United States)

    Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

    2018-04-01

    In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.

  16. Evolving RBF neural networks for adaptive soft-sensor design.

    Science.gov (United States)

    Alexandridis, Alex

    2013-12-01

    This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

  17. What is adapted in face adaptation? The neural representations of expression in the human visual system.

    Science.gov (United States)

    Fox, Christopher J; Barton, Jason J S

    2007-01-05

    The neural representation of facial expression within the human visual system is not well defined. Using an adaptation paradigm, we examined aftereffects on expression perception produced by various stimuli. Adapting to a face, which was used to create morphs between two expressions, substantially biased expression perception within the morphed faces away from the adapting expression. This adaptation was not based on low-level image properties, as a different image of the same person displaying that expression produced equally robust aftereffects. Smaller but significant aftereffects were generated by images of different individuals, irrespective of gender. Non-face visual, auditory, or verbal representations of emotion did not generate significant aftereffects. These results suggest that adaptation affects at least two neural representations of expression: one specific to the individual (not the image), and one that represents expression across different facial identities. The identity-independent aftereffect suggests the existence of a 'visual semantic' for facial expression in the human visual system.

  18. Neural controller for adaptive movements with unforeseen payloads.

    Science.gov (United States)

    Kuperstein, M; Wang, J

    1990-01-01

    A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

  19. Adaptive Moving Object Tracking Integrating Neural Networks And Intelligent Processing

    Science.gov (United States)

    Lee, James S. J.; Nguyen, Dziem D.; Lin, C.

    1989-03-01

    A real-time adaptive scheme is introduced to detect and track moving objects under noisy, dynamic conditions including moving sensors. This approach integrates the adaptiveness and incremental learning characteristics of neural networks with intelligent reasoning and process control. Spatiotemporal filtering is used to detect and analyze motion, exploiting the speed and accuracy of multiresolution processing. A neural network algorithm constitutes the basic computational structure for classification. A recognition and learning controller guides the on-line training of the network, and invokes pattern recognition to determine processing parameters dynamically and to verify detection results. A tracking controller acts as the central control unit, so that tracking goals direct the over-all system. Performance is benchmarked against the Widrow-Hoff algorithm, for target detection scenarios presented in diverse FLIR image sequences. Efficient algorithm design ensures that this recognition and control scheme, implemented in software and commercially available image processing hardware, meets the real-time requirements of tracking applications.

  20. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    Directory of Open Access Journals (Sweden)

    Poramate eManoonpong

    2013-02-01

    Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  1. Adaptive enhanced sampling by force-biasing using neural networks

    Science.gov (United States)

    Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

    2018-04-01

    A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

  2. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...... allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show...

  3. Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Kazuhiko Hiramoto

    2018-01-01

    Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

  4. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    Science.gov (United States)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  5. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  6. Algebraic and adaptive learning in neural control systems

    Science.gov (United States)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  7. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  8. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  9. Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

    Science.gov (United States)

    Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

    2016-11-14

    In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

  10. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  11. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  12. Disruption prediction with adaptive neural networks for ASDEX Upgrade

    International Nuclear Information System (INIS)

    Cannas, B.; Fanni, A.; Pautasso, G.; Sias, G.

    2011-01-01

    In this paper, an adaptive neural system has been built to predict the risk of disruption at ASDEX Upgrade. The system contains a Self Organizing Map, which determines the 'novelty' of the input of a Multi Layer Perceptron predictor module. The answer of the MLP predictor will be inhibited whenever a novel sample is detected. Furthermore, it is possible that the predictor produces a wrong answer although it is fed with known samples. In this case, a retraining procedure will be performed to update the MLP predictor in an incremental fashion using data coming from both the novelty detection, and from wrong predictions. In particular, a new update is performed whenever a missed alarm is triggered by the predictor. The performance of the adaptive predictor during the more recent experimental campaigns until November 2009 has been evaluated.

  13. Adaptive PID control based on orthogonal endocrine neural networks.

    Science.gov (United States)

    Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

    2016-12-01

    A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Adaptive control using neural networks and approximate models.

    Science.gov (United States)

    Narendra, K S; Mukhopadhyay, S

    1997-01-01

    The NARMA model is an exact representation of the input-output behavior of finite-dimensional nonlinear discrete-time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. In this paper, we introduce two classes of models which are approximations to the NARMA model, and which are linear in the control input. The latter fact substantially simplifies both the theoretical analysis as well as the practical implementation of the controller. Extensive simulation studies have shown that the neural controllers designed using the proposed approximate models perform very well, and in many cases even better than an approximate controller designed using the exact NARMA model. In view of their mathematical tractability as well as their success in simulation studies, a case is made in this paper that such approximate input-output models warrant a detailed study in their own right.

  15. Control of beam halo-chaos using neural network self-adaptation method

    International Nuclear Information System (INIS)

    Fang Jinqing; Huang Guoxian; Luo Xiaoshu

    2004-11-01

    Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

  16. A recurrent neural network for adaptive beamforming and array correction.

    Science.gov (United States)

    Che, Hangjun; Li, Chuandong; He, Xing; Huang, Tingwen

    2016-08-01

    In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Adaptive training of neural networks for control of autonomous mobile robots

    NARCIS (Netherlands)

    Steur, E.; Vromen, T.; Nijmeijer, H.; Fossen, T.I.; Nijmeijer, H.; Pettersen, K.Y.

    2017-01-01

    We present an adaptive training procedure for a spiking neural network, which is used for control of a mobile robot. Because of manufacturing tolerances, any hardware implementation of a spiking neural network has non-identical nodes, which limit the performance of the controller. The adaptive

  18. Neural networks and their potential application in nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

  19. Neural correlates of eating disorders: translational potential

    Directory of Open Access Journals (Sweden)

    McAdams CJ

    2015-09-01

    Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

  20. Fluid intelligence and neural mechanisms of conflict adaptation

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Jiannong, Shi

    2016-01-01

    The current study investigated whether adolescents with different intellectual levels have different conflict adaptation processes. Adolescents with high and average IQ abilities were enrolled, and their behavioral responses and event-related potentials (ERPs) were recorded during a modified...... Eriksen flanker task. Both groups showed reliable conflict adaptation effects (CAE) with regard to the reaction time (RT), and they showed a faster response to the cC condition than to the iC condition and faster response to the iI condition than to the cI condition. The IQ-related findings showed...... that high IQ adolescents had shorter RTs than their average-IQ counterparts in the cI, iC, and iI conditions, with smaller RT-CAE values. These findings indicated that high IQ adolescents had superior conflict adaptation processes. The electrophysiological findings showed that the cI condition required more...

  1. Neural networks - Potential appplication in the nuclear industry

    International Nuclear Information System (INIS)

    Yiftah, S.

    1989-01-01

    Neural networks are an emerging technology which is perceived to have potential for solving complex computation problems which cannot be solved by standard computational methods. One such example is the inverse kinematics problem which is considered to be the most difficult problem in robotics. In 1986, only one neural network modelling tool was available, now there are about twenty offered commercially by various companies in North America

  2. Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

    Science.gov (United States)

    Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

    2018-01-01

    In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

  3. Neural networks and their potential application to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

  4. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  5. Efficient computation in adaptive artificial spiking neural networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

    2017-01-01

    textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

  6. Adaptive fuzzy-neural-network control for maglev transportation system.

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

    A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

  7. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...

  8. Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling.

    Science.gov (United States)

    Yang, S; Wang, D

    2000-01-01

    This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.

  9. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  10. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is ... The robust controller is used to guarantee the stability and to control the per- ..... From the above analysis we have the following theorem:.

  11. NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

    Directory of Open Access Journals (Sweden)

    S. Serikov

    2012-01-01

    Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

  12. Potential Mechanisms and Functions of Intermittent Neural Synchronization

    Directory of Open Access Journals (Sweden)

    Sungwoo Ahn

    2017-05-01

    Full Text Available Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This “spikiness” is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.

  13. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  14. Communication: Fitting potential energy surfaces with fundamental invariant neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Kejie; Chen, Jun; Zhao, Zhiqiang; Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn [State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China and University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China. (China)

    2016-08-21

    A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

  15. Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

    Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

  16. Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

    Science.gov (United States)

    Mundell, Nathan A; Labosky, Patricia A

    2011-02-01

    Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

  17. Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

    Science.gov (United States)

    Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey

    2007-12-01

    In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).

  18. Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

    Directory of Open Access Journals (Sweden)

    Xiao-Li Li

    2014-01-01

    Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

  19. Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-02-01

    Full Text Available A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

  20. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  1. Predicting local field potentials with recurrent neural networks.

    Science.gov (United States)

    Kim, Louis; Harer, Jacob; Rangamani, Akshay; Moran, James; Parks, Philip D; Widge, Alik; Eskandar, Emad; Dougherty, Darin; Chin, Sang Peter

    2016-08-01

    We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting 10 and 100 millisecond forward in time.

  2. Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

    Science.gov (United States)

    Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

    2015-03-01

    Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

  3. The neural dynamics of conflict adaptation within a look-to-do transition.

    Directory of Open Access Journals (Sweden)

    Dandan Tang

    Full Text Available BACKGROUND: For optimal performance in conflict situations, conflict adaptation (conflict detection and adjustment is necessary. However, the neural dynamics of conflict adaptation is still unclear. METHODS: In the present study, behavioral and electroencephalography (EEG data were recorded from seventeen healthy participants during performance of a color-word Stroop task with a novel look-to-do transition. Within this transition, participants looked at the Stroop stimuli but no responses were required in the 'look' trials; or made manual responses to the Stroop stimuli in the 'do' trials. RESULTS: In the 'look' trials, the amplitude modulation of N450 occurred exclusively in the right-frontal region. Subsequently, the amplitude modulation of sustained potential (SP emerged in the posterior parietal and right-frontal regions. A significantly positive correlation between the modulation of reconfiguration in the 'look' trials and the behavioral conflict adaptation in the 'do' trials was observed. Specially, a stronger information flow from right-frontal region to posterior parietal region in the beta band was observed for incongruent condition than for congruent condition. In the 'do' trials, the conflict of 'look' trials enhanced the amplitude modulations of N450 in the right-frontal and posterior parietal regions, but decreased the amplitude modulations of SP in these regions. Uniquely, a stronger information flow from centro-parietal region to right-frontal region in the theta band was observed for iI condition than for cI condition. CONCLUSION: All these findings showed that top-down conflict adaptation is implemented by: (1 enhancing the sensitivity to conflict detection and the adaptation to conflict resolution; (2 modulating the effective connectivity between parietal region and right-frontal region.

  4. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    Science.gov (United States)

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

    Directory of Open Access Journals (Sweden)

    Chih-Hong Kao

    2011-01-01

    Full Text Available This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme.

  6. Neural and morphological adaptations of vastus lateralis and vastus medialis muscles to isokinetic eccentric training

    Directory of Open Access Journals (Sweden)

    Rodrigo de Azevedo Franke

    2014-09-01

    Full Text Available Vastus lateralis (VL and vastus medialis (VM are frequently targeted in conditioning/rehabilitation programs due to their role in patellar stabilization during knee extension. This study assessed neural and muscular adaptations in these two muscles after an isokinetic eccentric training program. Twenty healthy men underwent a four-week control period followed by a 12-week period of isokinetic eccentric training. Ultrasound evaluations of VL and VM muscle thickness at rest and electromyographic evaluations during maximal isometric tests were used to assess the morphological and neural properties, respectively. No morphological and neural changes were found throughout the control period, whereas both muscles showed significant increases in thickness (VL = 6.9%; p .05 post-training. Isokinetic eccentric training produces neural and greater morphological adaptations in VM compared to VL, which shows that synergistic muscles respond differently to an eccentric isokinetic strength training program

  7. Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

    OpenAIRE

    Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

    2017-01-01

    In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

  8. Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties

    Directory of Open Access Journals (Sweden)

    Yang Fang

    2014-01-01

    Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.

  9. Generalized Net Model of the Cognitive and Neural Algorithm for Adaptive Resonance Theory 1

    Directory of Open Access Journals (Sweden)

    Todor Petkov

    2013-12-01

    Full Text Available The artificial neural networks are inspired by biological properties of human and animal brains. One of the neural networks type is called ART [4]. The abbreviation of ART stands for Adaptive Resonance Theory that has been invented by Stephen Grossberg in 1976 [5]. ART represents a family of Neural Networks. It is a cognitive and neural theory that describes how the brain autonomously learns to categorize, recognize and predict objects and events in the changing world. In this paper we introduce a GN model that represent ART1 Neural Network learning algorithm [1]. The purpose of this model is to explain when the input vector will be clustered or rejected among all nodes by the network. It can also be used for explanation and optimization of ART1 learning algorithm.

  10. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    Directory of Open Access Journals (Sweden)

    Eduard eGrinke

    2015-10-01

    Full Text Available Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments.

  11. Music listening after stroke: beneficial effects and potential neural mechanisms.

    Science.gov (United States)

    Särkämö, Teppo; Soto, David

    2012-04-01

    Music is an enjoyable leisure activity that also engages many emotional, cognitive, and motor processes in the brain. Here, we will first review previous literature on the emotional and cognitive effects of music listening in healthy persons and various clinical groups. Then we will present findings about the short- and long-term effects of music listening on the recovery of cognitive function in stroke patients and the underlying neural mechanisms of these music effects. First, our results indicate that listening to pleasant music can have a short-term facilitating effect on visual awareness in patients with visual neglect, which is associated with functional coupling between emotional and attentional brain regions. Second, daily music listening can improve auditory and verbal memory, focused attention, and mood as well as induce structural gray matter changes in the early poststroke stage. The psychological and neural mechanisms potentially underlying the rehabilitating effect of music after stroke are discussed. © 2012 New York Academy of Sciences.

  12. A Novel Adaptive Joint Time Frequency Algorithm by the Neural Network for the ISAR Rotational Compensation

    Directory of Open Access Journals (Sweden)

    Zisheng Wang

    2018-02-01

    Full Text Available We propose a novel adaptive joint time frequency algorithm combined with the neural network (AJTF-NN to focus the distorted inverse synthetic aperture radar (ISAR image. In this paper, a coefficient estimator based on the artificial neural network (ANN is firstly developed to solve the time-consuming rotational motion compensation (RMC polynomial phase coefficient estimation problem. The training method, the cost function and the structure of ANN are comprehensively discussed. In addition, we originally propose a method to generate training dataset sourcing from the ISAR signal models with randomly chosen motion characteristics. Then, prediction results of the ANN estimator is used to directly compensate the ISAR image, or to provide a more accurate initial searching range to the AJTF for possible low-performance scenarios. Finally, some simulation models including the ideal point scatterers and a realistic Airbus A380 are employed to comprehensively investigate properties of the AJTF-NN, such as the stability and the efficiency under different signal-to-noise ratios (SNRs. Results show that the proposed method is much faster than other prevalent improved searching methods, the acceleration ratio are even up to 424 times without the deterioration of compensated image quality. Therefore, the proposed method is potential to the real-time application in the RMC problem of the ISAR imaging.

  13. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Yundi Chu

    2015-01-01

    Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.

  14. Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms

    International Nuclear Information System (INIS)

    Sheng Li; Yang Huizhong; Lou Xuyang

    2009-01-01

    This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.

  15. Robust synchronization of delayed neural networks based on adaptive control and parameters identification

    International Nuclear Information System (INIS)

    Zhou Jin; Chen Tianping; Xiang Lan

    2006-01-01

    This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

  16. Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue.

    Directory of Open Access Journals (Sweden)

    Geir Halnes

    2016-11-01

    Full Text Available Recorded potentials in the extracellular space (ECS of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. The framework uses (1 the NEURON simulator to compute the activity and ionic output currents from multicompartmental neuron models, and (2 the electrodiffusive Kirchhoff-Nernst-Planck framework to simulate the resulting dynamics of the potential and ion concentrations in the ECS, accounting for the effect of electrical migration as well as diffusion. Using this framework, we explore the effect that ECS diffusion has on the electrical potential surrounding a small population of 10 pyramidal neurons. The neural model was tuned so that simulations over ∼100 seconds of biological time led to shifts in ECS concentrations by a few millimolars, similar to what has been seen in experiments. By comparing simulations where ECS diffusion was absent with simulations where ECS diffusion was included, we made the following key findings: (i ECS diffusion shifted the local potential by up to ∼0.2 mV. (ii The power spectral density (PSD of the diffusion-evoked potential shifts followed a 1/f2 power law. (iii Diffusion effects dominated the PSD of the ECS potential for frequencies up to several hertz. In scenarios with large, but physiologically realistic ECS concentration gradients, diffusion was thus found to affect the ECS potential well within the frequency range picked up in

  17. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

    Science.gov (United States)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles

  18. Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

    Directory of Open Access Journals (Sweden)

    Yanchao Yin

    2017-01-01

    Full Text Available A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA. Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

  19. Projective synchronization of time-varying delayed neural network with adaptive scaling factors

    International Nuclear Information System (INIS)

    Ghosh, Dibakar; Banerjee, Santo

    2013-01-01

    Highlights: • Projective synchronization in coupled delayed neural chaotic systems with modulated delay time is introduced. • An adaptive rule for the scaling factors is introduced. • This scheme is highly applicable in secure communication. -- Abstract: In this work, the projective synchronization between two continuous time delayed neural systems with time varying delay is investigated. A sufficient condition for synchronization for the coupled systems with modulated delay is presented analytically with the help of the Krasovskii–Lyapunov approach. The effect of adaptive scaling factors on synchronization are also studied in details. Numerical simulations verify the effectiveness of the analytic results

  20. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks.

    Science.gov (United States)

    Shen, Lin; Yang, Weitao

    2018-03-13

    Direct molecular dynamics (MD) simulation with ab initio quantum mechanical and molecular mechanical (QM/MM) methods is very powerful for studying the mechanism of chemical reactions in a complex environment but also very time-consuming. The computational cost of QM/MM calculations during MD simulations can be reduced significantly using semiempirical QM/MM methods with lower accuracy. To achieve higher accuracy at the ab initio QM/MM level, a correction on the existing semiempirical QM/MM model is an attractive idea. Recently, we reported a neural network (NN) method as QM/MM-NN to predict the potential energy difference between semiempirical and ab initio QM/MM approaches. The high-level results can be obtained using neural network based on semiempirical QM/MM MD simulations, but the lack of direct MD samplings at the ab initio QM/MM level is still a deficiency that limits the applications of QM/MM-NN. In the present paper, we developed a dynamic scheme of QM/MM-NN for direct MD simulations on the NN-predicted potential energy surface to approximate ab initio QM/MM MD. Since some configurations excluded from the database for NN training were encountered during simulations, which may cause some difficulties on MD samplings, an adaptive procedure inspired by the selection scheme reported by Behler [ Behler Int. J. Quantum Chem. 2015 , 115 , 1032 ; Behler Angew. Chem., Int. Ed. 2017 , 56 , 12828 ] was employed with some adaptions to update NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN MD method to the free energy calculation and transition path optimization on chemical reactions in water. The results at the ab initio QM/MM level can be well reproduced using this method after 2-4 iteration cycles. The saving in computational cost is about 2 orders of magnitude. It demonstrates that the QM/MM-NN with direct MD simulations has great potentials not only for the calculation of thermodynamic properties but also for the characterization of

  1. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  2. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  3. Neural adaptive control for vibration suppression in composite fin-tip of aircraft.

    Science.gov (United States)

    Suresh, S; Kannan, N; Sundararajan, N; Saratchandran, P

    2008-06-01

    In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

  4. Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics

    OpenAIRE

    Ho Pham Huy Anh; Nguyen Thanh Nam

    2012-01-01

    In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3‐DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model‐based identification process using experimental input‐output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural M...

  5. A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

    Science.gov (United States)

    Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

    2011-01-01

    To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

  6. Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

    Directory of Open Access Journals (Sweden)

    Yuzheng Yang

    2014-01-01

    Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

  7. Adaptive online state-of-charge determination based on neuro-controller and neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

    2010-05-15

    This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

  8. Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

    Directory of Open Access Journals (Sweden)

    Rong Mei

    2017-01-01

    Full Text Available This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

  9. Adaptive control using a hybrid-neural model: application to a polymerisation reactor

    Directory of Open Access Journals (Sweden)

    Cubillos F.

    2001-01-01

    Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.

  10. Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

    Science.gov (United States)

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

  11. Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

    Science.gov (United States)

    Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

    2010-09-01

    This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

  12. A simple mechanical system for studying adaptive oscillatory neural networks

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    Central Pattern Generators (CPG) are oscillatory systems that are responsible for generating rhythmic patterns at the origin of many biological activities such as for example locomotion or digestion. These systems are generally modelled as recurrent neural networks whose parameters are tuned so...... that the network oscillates in a suitable way, this tuning being a non trivial task. It also appears that the link with the physical body that these oscillatory entities control has a fundamental importance, and it seems that most bodies used for experimental validation in the literature (walking robots, lamprey...... a brief description of the Roller-Racer, we present as a preliminary study an RNN-based feed-forward controller whose parameters are obtained through the well-known teacher forcing learning algorithm, extended to learn signals with a continuous component....

  13. Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model.

    Science.gov (United States)

    Westendorff, Stephanie; Kuang, Shenbing; Taghizadeh, Bahareh; Donchin, Opher; Gail, Alexander

    2015-04-01

    Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement ("jump") consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation. Copyright © 2015 the American Physiological Society.

  14. Crowd counting via scale-adaptive convolutional neural network

    OpenAIRE

    Zhang, Lu; Shi, Miaojing; Chen, Qiaobo

    2017-01-01

    The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fi...

  15. A Dung Beetle-like Leg and its Adaptive Neural Control

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Stoyanov, Stoyan; Larsen, Jørgen Christian

    2016-01-01

    Dung beetles show fascinating locomotion abilities. They can use their legs to not only walk but also manipulate objects. Furthermore, they can perform their leg movements at a proper frequency with respect to their biomechanical properties and quickly adapt the movements to deal with external pe...... also apply adaptive neural control, based on a central pattern generator (CPG) circuit with synaptic plasticity, to autonomously generate a proper stepping frequency of the leg. The controller can also adapt the leg movement to deal with external perturbations within a few steps....

  16. Physiological and Neural Adaptations to Eccentric Exercise: Mechanisms and Considerations for Training

    Directory of Open Access Journals (Sweden)

    Nosratollah Hedayatpour

    2015-01-01

    Full Text Available Eccentric exercise is characterized by initial unfavorable effects such as subcellular muscle damage, pain, reduced fiber excitability, and initial muscle weakness. However, stretch combined with overload, as in eccentric contractions, is an effective stimulus for inducing physiological and neural adaptations to training. Eccentric exercise-induced adaptations include muscle hypertrophy, increased cortical activity, and changes in motor unit behavior, all of which contribute to improved muscle function. In this brief review, neuromuscular adaptations to different forms of exercise are reviewed, the positive training effects of eccentric exercise are presented, and the implications for training are considered.

  17. Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

    Science.gov (United States)

    Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

    2017-09-01

    This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

  18. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  19. Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

    Directory of Open Access Journals (Sweden)

    Wang Chao

    2016-03-01

    Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

  20. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  1. Biophysical characteristics reveal neural stem cell differentiation potential.

    Directory of Open Access Journals (Sweden)

    Fatima H Labeed

    Full Text Available Distinguishing human neural stem/progenitor cell (huNSPC populations that will predominantly generate neurons from those that produce glia is currently hampered by a lack of sufficient cell type-specific surface markers predictive of fate potential. This limits investigation of lineage-biased progenitors and their potential use as therapeutic agents. A live-cell biophysical and label-free measure of fate potential would solve this problem by obviating the need for specific cell surface markers.We used dielectrophoresis (DEP to analyze the biophysical, specifically electrophysiological, properties of cortical human and mouse NSPCs that vary in differentiation potential. Our data demonstrate that the electrophysiological property membrane capacitance inversely correlates with the neurogenic potential of NSPCs. Furthermore, as huNSPCs are continually passaged they decrease neuron generation and increase membrane capacitance, confirming that this parameter dynamically predicts and negatively correlates with neurogenic potential. In contrast, differences in membrane conductance between NSPCs do not consistently correlate with the ability of the cells to generate neurons. DEP crossover frequency, which is a quantitative measure of cell behavior in DEP, directly correlates with neuron generation of NSPCs, indicating a potential mechanism to separate stem cells biased to particular differentiated cell fates.We show here that whole cell membrane capacitance, but not membrane conductance, reflects and predicts the neurogenic potential of human and mouse NSPCs. Stem cell biophysical characteristics therefore provide a completely novel and quantitative measure of stem cell fate potential and a label-free means to identify neuron- or glial-biased progenitors.

  2. Neural communication patterns underlying conflict detection, resolution, and adaptation.

    Science.gov (United States)

    Oehrn, Carina R; Hanslmayr, Simon; Fell, Juergen; Deuker, Lorena; Kremers, Nico A; Do Lam, Anne T; Elger, Christian E; Axmacher, Nikolai

    2014-07-30

    In an ever-changing environment, selecting appropriate responses in conflicting situations is essential for biological survival and social success and requires cognitive control, which is mediated by dorsomedial prefrontal cortex (DMPFC) and dorsolateral prefrontal cortex (DLPFC). How these brain regions communicate during conflict processing (detection, resolution, and adaptation), however, is still unknown. The Stroop task provides a well-established paradigm to investigate the cognitive mechanisms mediating such response conflict. Here, we explore the oscillatory patterns within and between the DMPFC and DLPFC in human epilepsy patients with intracranial EEG electrodes during an auditory Stroop experiment. Data from the DLPFC were obtained from 12 patients. Thereof four patients had additional DMPFC electrodes available for interaction analyses. Our results show that an early θ (4-8 Hz) modulated enhancement of DLPFC γ-band (30-100 Hz) activity constituted a prerequisite for later successful conflict processing. Subsequent conflict detection was reflected in a DMPFC θ power increase that causally entrained DLPFC θ activity (DMPFC to DLPFC). Conflict resolution was thereafter completed by coupling of DLPFC γ power to DMPFC θ oscillations. Finally, conflict adaptation was related to increased postresponse DLPFC γ-band activity and to θ coupling in the reverse direction (DLPFC to DMPFC). These results draw a detailed picture on how two regions in the prefrontal cortex communicate to resolve cognitive conflicts. In conclusion, our data show that conflict detection, control, and adaptation are supported by a sequence of processes that use the interplay of θ and γ oscillations within and between DMPFC and DLPFC. Copyright © 2014 the authors 0270-6474/14/3410438-15$15.00/0.

  3. Adaptive Smoothing in fMRI Data Processing Neural Networks

    DEFF Research Database (Denmark)

    Vilamala, Albert; Madsen, Kristoffer Hougaard; Hansen, Lars Kai

    2017-01-01

    in isolation. With the advent of new tools for deep learning, recent work has proposed to turn these pipelines into end-to-end learning networks. This change of paradigm offers new avenues to improvement as it allows for a global optimisation. The current work aims at benefitting from this paradigm shift...... by defining a smoothing step as a layer in these networks able to adaptively modulate the degree of smoothing required by each brain volume to better accomplish a given data analysis task. The viability is evaluated on real fMRI data where subjects did alternate between left and right finger tapping tasks....

  4. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    Science.gov (United States)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  5. Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels

    Directory of Open Access Journals (Sweden)

    Abbas Ajorkar

    2015-04-01

    Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.

  6. Hardware implementation of an adaptive resonance theory (ART) neural network using compensated operational amplifiers

    Science.gov (United States)

    Ho, Ching S.; Liou, Juin J.; Georgiopoulos, Michael; Christodoulou, Christos G.

    1994-03-01

    This paper presents an analog circuit design and implementation for an adaptive resonance theory neural network architecture called the augmented ART1 neural network (AART1-NN). Practical monolithic operational amplifiers (Op-Amps) LM741 and LM318 are selected to implement the circuit, and a simple compensation scheme is developed to adjust the Op-Amp electrical characteristics to meet the design requirement. A 7-node prototype circuit has been designed and verified using the Pspice circuit simulator run on a Sun workstation. Results simulated from the AART1-NN circuit using the LM741, LM318, and ideal Op-Amps are presented and compared.

  7. Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2017-01-01

    Full Text Available This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

  8. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  9. Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

    Science.gov (United States)

    Niu, Ben; Li, Lu

    2018-06-01

    This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

  10. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    International Nuclear Information System (INIS)

    Husam Fayiz, Al Masri

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)

  11. Neural network potential for Al-Mg-Si alloys

    Science.gov (United States)

    Kobayashi, Ryo; Giofré, Daniele; Junge, Till; Ceriotti, Michele; Curtin, William A.

    2017-10-01

    The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitation. To enable atomistic-level simulations of both the processing and performance of this important alloy system, a neural network (NN) potential for the ternary Al-Mg-Si has been created. Training of the NN uses an extensive database of properties computed using first-principles density functional theory, including complex precipitate phases in this alloy. The NN potential accurately reproduces most of the pure Al properties relevant to the mechanical behavior as well as heat of solution, solute-solute, and solute-vacancy interaction energies, and formation energies of small solute clusters and precipitates that are required for modeling the early stage of precipitation and mechanical strengthening. This success not only enables future detailed studies of Al-Mg-Si but also highlights the ability of NN methods to generate useful potentials in complex alloy systems.

  12. Neural mechanisms underlying spatial realignment during adaptation to optical wedge prisms.

    Science.gov (United States)

    Chapman, Heidi L; Eramudugolla, Ranmalee; Gavrilescu, Maria; Strudwick, Mark W; Loftus, Andrea; Cunnington, Ross; Mattingley, Jason B

    2010-07-01

    Visuomotor adaptation to a shift in visual input produced by prismatic lenses is an example of dynamic sensory-motor plasticity within the brain. Prism adaptation is readily induced in healthy individuals, and is thought to reflect the brain's ability to compensate for drifts in spatial calibration between different sensory systems. The neural correlate of this form of functional plasticity is largely unknown, although current models predict the involvement of parieto-cerebellar circuits. Recent studies that have employed event-related functional magnetic resonance imaging (fMRI) to identify brain regions associated with prism adaptation have discovered patterns of parietal and cerebellar modulation as participants corrected their visuomotor errors during the early part of adaptation. However, the role of these regions in the later stage of adaptation, when 'spatial realignment' or true adaptation is predicted to occur, remains unclear. Here, we used fMRI to quantify the distinctive patterns of parieto-cerebellar activity as visuomotor adaptation develops. We directly contrasted activation patterns during the initial error correction phase of visuomotor adaptation with that during the later spatial realignment phase, and found significant recruitment of the parieto-cerebellar network--with activations in the right inferior parietal lobe and the right posterior cerebellum. These findings provide the first evidence of both cerebellar and parietal involvement during the spatial realignment phase of prism adaptation. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

  13. Study on application of adaptive fuzzy control and neural network in the automatic leveling system

    Science.gov (United States)

    Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

    2015-04-01

    This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

  14. Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

    International Nuclear Information System (INIS)

    Mai, Huanhuan; Liao, Xiaofeng; Song, Gangbing

    2013-01-01

    Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller. (paper)

  15. Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

    Science.gov (United States)

    Mai, Huanhuan; Song, Gangbing; Liao, Xiaofeng

    2013-01-01

    Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

  16. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  17. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  18. Identifying product development crises: The potential of adaptive heuristics

    DEFF Research Database (Denmark)

    Münzberger, C.; Stingl, Verena; Oehmen, Josef

    2017-01-01

    This paper introduces adaptive heuristics as a tool to identify crises in design projects and highlights potential applications of these heuristics as decision support tool for crisis identification. Crises may emerge slowly or suddenly, and often have ambiguous signals. Thus the identification...... for the application of heuristics in design sciences. To achieve this, the paper compares crises to 'business as usual', and presents sixteen indicators for emerging crises. These indicators are potential cues for adaptive heuristics. Specifically three adaptive heuristics, One-single-cue, Fast-and-Frugal-Trees...

  19. An adaptive interpolation scheme for molecular potential energy surfaces

    Science.gov (United States)

    Kowalewski, Markus; Larsson, Elisabeth; Heryudono, Alfa

    2016-08-01

    The calculation of potential energy surfaces for quantum dynamics can be a time consuming task—especially when a high level of theory for the electronic structure calculation is required. We propose an adaptive interpolation algorithm based on polyharmonic splines combined with a partition of unity approach. The adaptive node refinement allows to greatly reduce the number of sample points by employing a local error estimate. The algorithm and its scaling behavior are evaluated for a model function in 2, 3, and 4 dimensions. The developed algorithm allows for a more rapid and reliable interpolation of a potential energy surface within a given accuracy compared to the non-adaptive version.

  20. Face Recognition by Bunch Graph Method Using a Group Based Adaptive Tolerant Neural Network

    OpenAIRE

    Aradhana D.; Girish H.; Karibasappa K.; Reddy A. Chennakeshava

    2011-01-01

    This paper presents a new method for feature extraction from the facial image by using bunch graph method. These extracted geometric features of the face are used subsequently for face recognition by utilizing the group based adaptive neural network. This method is suitable, when the facial images are rotation and translation invariant. Further the technique also free from size invariance of facial image and is capable of identifying the facial images correctly when corrupted w...

  1. Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

  2. Epithelium-Stroma Classification via Convolutional Neural Networks and Unsupervised Domain Adaptation in Histopathological Images.

    Science.gov (United States)

    Huang, Yue; Zheng, Han; Liu, Chi; Ding, Xinghao; Rohde, Gustavo K

    2017-11-01

    Epithelium-stroma classification is a necessary preprocessing step in histopathological image analysis. Current deep learning based recognition methods for histology data require collection of large volumes of labeled data in order to train a new neural network when there are changes to the image acquisition procedure. However, it is extremely expensive for pathologists to manually label sufficient volumes of data for each pathology study in a professional manner, which results in limitations in real-world applications. A very simple but effective deep learning method, that introduces the concept of unsupervised domain adaptation to a simple convolutional neural network (CNN), has been proposed in this paper. Inspired by transfer learning, our paper assumes that the training data and testing data follow different distributions, and there is an adaptation operation to more accurately estimate the kernels in CNN in feature extraction, in order to enhance performance by transferring knowledge from labeled data in source domain to unlabeled data in target domain. The model has been evaluated using three independent public epithelium-stroma datasets by cross-dataset validations. The experimental results demonstrate that for epithelium-stroma classification, the proposed framework outperforms the state-of-the-art deep neural network model, and it also achieves better performance than other existing deep domain adaptation methods. The proposed model can be considered to be a better option for real-world applications in histopathological image analysis, since there is no longer a requirement for large-scale labeled data in each specified domain.

  3. Neuroplasticity beyond Sounds: Neural Adaptations Following Long-Term Musical Aesthetic Experiences

    Directory of Open Access Journals (Sweden)

    Mark Reybrouck

    2015-03-01

    Full Text Available Capitalizing from neuroscience knowledge on how individuals are affected by the sound environment, we propose to adopt a cybernetic and ecological point of view on the musical aesthetic experience, which includes subprocesses, such as feature extraction and integration, early affective reactions and motor actions, style mastering and conceptualization, emotion and proprioception, evaluation and preference. In this perspective, the role of the listener/composer/performer is seen as that of an active “agent” coping in highly individual ways with the sounds. The findings concerning the neural adaptations in musicians, following long-term exposure to music, are then reviewed by keeping in mind the distinct subprocesses of a musical aesthetic experience. We conclude that these neural adaptations can be conceived of as the immediate and lifelong interactions with multisensorial stimuli (having a predominant auditory component, which result in lasting changes of the internal state of the “agent”. In a continuous loop, these changes affect, in turn, the subprocesses involved in a musical aesthetic experience, towards the final goal of achieving better perceptual, motor and proprioceptive responses to the immediate demands of the sounding environment. The resulting neural adaptations in musicians closely depend on the duration of the interactions, the starting age, the involvement of attention, the amount of motor practice and the musical genre played.

  4. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  5. An indirect adaptive neural control of a visual-based quadrotor robot for pursuing a moving target.

    Science.gov (United States)

    Shirzadeh, Masoud; Amirkhani, Abdollah; Jalali, Aliakbar; Mosavi, Mohammad R

    2015-11-01

    This paper aims to use a visual-based control mechanism to control a quadrotor type aerial robot which is in pursuit of a moving target. The nonlinear nature of a quadrotor, on the one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on artificial neural networks and other similar approaches. In addition to the two mentioned problems, another problem that emerges due to the moving nature of a target is the uncertainty that exists in the target image. By employing an artificial neural network with a Radial Basis Function (RBF) an indirect adaptive neural controller has been designed for a quadrotor robot in search of a moving target. The results of the simulation for different paths show that the quadrotor has efficiently tracked the moving target. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  6. Adaptive control of nonlinear system using online error minimum neural networks.

    Science.gov (United States)

    Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

    2016-11-01

    In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  7. TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

    DEFF Research Database (Denmark)

    Yao, Wei; Fang, Jiakun; Zhao, Ping

    2013-01-01

    the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency......In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...

  8. Neurodevelopment of Conflict Adaptation: Evidence From Event-Related Potentials

    DEFF Research Database (Denmark)

    Liu, Xiuying; Liu, Tongran; Shangguan, Fangfang

    2018-01-01

    Conflict adaptation is key in how children self-regulate and assert cognitive control in a given situation compared with a previous experience. In the current study, we analyzed event-related potentials (ERPs) to identify age-related differences in conflict adaptation. Participants of different a...... to better assimilate and accommodate potential environmental conflicts. The results may also indicate that the development of conflict adaption is affected by the specific characteristic of the different types of conflict.......Conflict adaptation is key in how children self-regulate and assert cognitive control in a given situation compared with a previous experience. In the current study, we analyzed event-related potentials (ERPs) to identify age-related differences in conflict adaptation. Participants of different...... ages (5-year-old children, 10-year-old children, and adults) were subjected to a stimulus-stimulus (S-S) conflict control task (the flanker task) and a stimulus-response (S-R) conflict control task (the Simon task). The behavioral results revealed that all age groups had reliable conflict adaptation...

  9. Potential applications of neural networks to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: diagnosing specific abnormal conditions, detection of the change of mode of operation, signal validation, monitoring of check valves, plant-wide monitoring using autoassociative neural networks, modeling of the plant thermodynamics, emulation of core reload calculations, monitoring of plant parameters, and analysis of plant vibrations. Each of these projects and its status are described briefly in this article. The objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks

  10. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

    In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

  11. Neural network for adapting nuclear power plant control for wide-range operation

    International Nuclear Information System (INIS)

    Ku, C.C.; Lee, K.Y.; Edwards, R.M.

    1991-01-01

    A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics

  12. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  13. A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

    Science.gov (United States)

    Zhao, Haiquan; Zhang, Jiashu

    2009-12-01

    To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

  14. ISC feedforward control of gasoline engine. Adaptive system using neural network; Jidoshayo gasoline engine no ISC feedforward seigyo. Neural network wo mochiita tekioka

    Energy Technology Data Exchange (ETDEWEB)

    Kinugawa, N; Morita, S; Takiyama, T [Osaka City University, Osaka (Japan)

    1997-10-01

    For fuel economy and a good driver`s feeling, it is necessary for idle-speed to keep at a constant low speed. But keeping low speed has danger of engine stall when the engine torque is disturbed by the alternator, and so on. In this paper, adaptive feedforward idle-speed control system against electrical loads was investigated. This system was based on the reversed tansfer functions of the object system, and a neural network was used to adapt this system for aging. Then, this neural network was also used for creating feedforward table map. Good experimental results were obtained. 2 refs., 11 figs.

  15. Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics

    Directory of Open Access Journals (Sweden)

    Ho Pham Huy Anh

    2012-10-01

    Full Text Available In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3-DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model-based identification process using experimental input-output training data. This paper proposes a novel use of a back propagation (BP algorithm to generate the forward neural MIMO NARX (FNMN model for the forward kinematics of the industrial 3-DOF robot arm. The results show that the proposed adaptive neural NARX model trained by a Back Propagation learning algorithm yields outstanding performance and perfect accuracy.

  16. Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

    2014-01-01

    Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... for generating basic rhythmic patterns and coordinated movements, 2) reservoir-based adaptive forward models with efference copies for sensory prediction as well as state estimation, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental...

  17. Immature visual neural system in children reflected by contrast sensitivity with adaptive optics correction

    Science.gov (United States)

    Liu, Rong; Zhou, Jiawei; Zhao, Haoxin; Dai, Yun; Zhang, Yudong; Tang, Yong; Zhou, Yifeng

    2014-01-01

    This study aimed to explore the neural development status of the visual system of children (around 8 years old) using contrast sensitivity. We achieved this by eliminating the influence of higher order aberrations (HOAs) with adaptive optics correction. We measured HOAs, modulation transfer functions (MTFs) and contrast sensitivity functions (CSFs) of six children and five adults with both corrected and uncorrected HOAs. We found that when HOAs were corrected, children and adults both showed improvements in MTF and CSF. However, the CSF of children was still lower than the adult level, indicating the difference in contrast sensitivity between groups cannot be explained by differences in optical factors. Further study showed that the difference between the groups also could not be explained by differences in non-visual factors. With these results we concluded that the neural systems underlying vision in children of around 8 years old are still immature in contrast sensitivity. PMID:24732728

  18. An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

    International Nuclear Information System (INIS)

    Avci, E.

    2007-01-01

    In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

  19. Novel adaptive neural control of flexible air-breathing hypersonic vehicles based on sliding mode differentiator

    Directory of Open Access Journals (Sweden)

    Bu Xiangwei

    2015-08-01

    Full Text Available A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle (FAHV. By utilizing functional decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude subsystem. For each subsystem, only one neural network is employed for the unknown function approximation. To further reduce the computational burden, minimal-learning parameter (MLP technology is used to estimate the norm of ideal weight vectors rather than their elements. By introducing sliding mode differentiator (SMD to estimate the newly defined variables, there is no need for the strict-feedback form and virtual controller. Hence the developed control law is considerably simpler than the ones derived from back-stepping scheme. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.

  20. Growing adaptive machines combining development and learning in artificial neural networks

    CERN Document Server

    Bredeche, Nicolas; Doursat, René

    2014-01-01

    The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs, and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a...

  1. Adaptive Neural Back-Stepping Control with Constrains for a Flexible Air-Breathing Hypersonic Vehicle

    Directory of Open Access Journals (Sweden)

    Pengfei Wang

    2015-01-01

    Full Text Available The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances.

  2. Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-01-01

    Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.

  3. Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

    Science.gov (United States)

    Crago, Patrick E; Makowski, Nathaniel S

    2014-10-01

    Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

  4. Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

    Science.gov (United States)

    Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

    2017-01-01

    Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces. PMID:29046625

  5. Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

    Directory of Open Access Journals (Sweden)

    Irina-Emilia Nicolae

    2017-10-01

    Full Text Available Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain.Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing within three distinct domains (memory, language, and visual imagination. Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs and in the extend and duration of event-related desynchronization (ERD which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing.Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs.Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

  6. Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation.

    Science.gov (United States)

    Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

    2017-01-01

    Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

  7. The Potential of Adaptive Design in Animal Studies

    Directory of Open Access Journals (Sweden)

    Arshad Majid

    2015-10-01

    Full Text Available Clinical trials are the backbone of medical research, and are often the last step in the development of new therapies for use in patients. Prior to human testing, however, preclinical studies using animal subjects are usually performed in order to provide initial data on the safety and effectiveness of prospective treatments. These studies can be costly and time consuming, and may also raise concerns about the ethical treatment of animals when potentially harmful procedures are involved. Adaptive design is a process by which the methods used in a study may be altered while it is being conducted in response to preliminary data or other new information. Adaptive design has been shown to be useful in reducing the time and costs associated with clinical trials, and may provide similar benefits in preclinical animal studies. The purpose of this review is to summarize various aspects of adaptive design and evaluate its potential for use in preclinical research.

  8. The Potential of Adaptive Design in Animal Studies.

    Science.gov (United States)

    Majid, Arshad; Bae, Ok-Nam; Redgrave, Jessica; Teare, Dawn; Ali, Ali; Zemke, Daniel

    2015-10-12

    Clinical trials are the backbone of medical research, and are often the last step in the development of new therapies for use in patients. Prior to human testing, however, preclinical studies using animal subjects are usually performed in order to provide initial data on the safety and effectiveness of prospective treatments. These studies can be costly and time consuming, and may also raise concerns about the ethical treatment of animals when potentially harmful procedures are involved. Adaptive design is a process by which the methods used in a study may be altered while it is being conducted in response to preliminary data or other new information. Adaptive design has been shown to be useful in reducing the time and costs associated with clinical trials, and may provide similar benefits in preclinical animal studies. The purpose of this review is to summarize various aspects of adaptive design and evaluate its potential for use in preclinical research.

  9. Using brain potentials to understand prism adaptation: the error-related negativity and the P300

    Directory of Open Access Journals (Sweden)

    Stephane Joseph Maclean

    2015-06-01

    Full Text Available Prism adaptation (PA is both a perceptual-motor learning task as well as a promising rehabilitation tool for visuo-spatial neglect (VSN – a spatial attention disorder often experienced after stroke resulting in slowed and/or inaccurate motor responses to contralesional targets. During PA, individuals are exposed to prism-induced shifts of the visual-field while performing a visuo-guided reaching task. After adaptation, with goggles removed, visuo-motor responding is shifted to the opposite direction of that initially induced by the prisms. This visuo-motor aftereffect has been used to study visuo-motor learning and adaptation and has been applied clinically to reduce VSN severity by improving motor responding to stimuli in contralesional (usually left-sided space. In order to optimize PA’s use for VSN patients, it is important to elucidate the neural and cognitive processes that alter visuomotor function during PA. In the present study, healthy young adults underwent PA while event-related potentials (ERPs were recorded at the termination of each reach (screen-touch, then binned according to accuracy (hit vs. miss and phase of exposure block (early, middle, late. Results show that two ERP components were evoked by screen-touch: an early error-related negativity (ERN, and a P300. The ERN was consistently evoked on miss trials during adaptation, while the P300 amplitude was largest during the early phase of adaptation for both hit and miss trials. This study provides evidence of two neural signals sensitive to visual feedback during PA that may sub-serve changes in visuomotor responding. Prior ERP research suggests that the ERN reflects an error processing system in medial-frontal cortex, while the P300 is suggested to reflect a system for context updating and learning. Future research is needed to elucidate the role of these ERP components in improving visuomotor responses among individuals with VSN.

  10. Using brain potentials to understand prism adaptation: the error-related negativity and the P300.

    Science.gov (United States)

    MacLean, Stephane J; Hassall, Cameron D; Ishigami, Yoko; Krigolson, Olav E; Eskes, Gail A

    2015-01-01

    Prism adaptation (PA) is both a perceptual-motor learning task as well as a promising rehabilitation tool for visuo-spatial neglect (VSN)-a spatial attention disorder often experienced after stroke resulting in slowed and/or inaccurate motor responses to contralesional targets. During PA, individuals are exposed to prism-induced shifts of the visual-field while performing a visuo-guided reaching task. After adaptation, with goggles removed, visuomotor responding is shifted to the opposite direction of that initially induced by the prisms. This visuomotor aftereffect has been used to study visuomotor learning and adaptation and has been applied clinically to reduce VSN severity by improving motor responding to stimuli in contralesional (usually left-sided) space. In order to optimize PA's use for VSN patients, it is important to elucidate the neural and cognitive processes that alter visuomotor function during PA. In the present study, healthy young adults underwent PA while event-related potentials (ERPs) were recorded at the termination of each reach (screen-touch), then binned according to accuracy (hit vs. miss) and phase of exposure block (early, middle, late). Results show that two ERP components were evoked by screen-touch: an error-related negativity (ERN), and a P300. The ERN was consistently evoked on miss trials during adaptation, while the P300 amplitude was largest during the early phase of adaptation for both hit and miss trials. This study provides evidence of two neural signals sensitive to visual feedback during PA that may sub-serve changes in visuomotor responding. Prior ERP research suggests that the ERN reflects an error processing system in medial-frontal cortex, while the P300 is suggested to reflect a system for context updating and learning. Future research is needed to elucidate the role of these ERP components in improving visuomotor responses among individuals with VSN.

  11. [Neural Mechanisms That Facilitate Adaptive Behavior Based on Acquired Stimulus-Outcome Information].

    Science.gov (United States)

    Ogawa, Masaaki

    2017-11-01

    In response to changing internal and external situations, we always need to adapt our behavior based on previous experiences, particularly, acquired stimulus-outcome information. The orbitofrontal cortex (OFC), a prefrontal cortical region, is critical for this type of decision-making. The current understanding of the fundamental functions of the OFC has been reviewed by introducing, as an example, how the OFC contributes to the processing of uncertain rewards. Furthermore, the importance of revealing context and temporally specific causal roles of neural circuits including the OFC in decision-making, as well as the techniques to achieve the goal, have been discussed.

  12. Performance assessment of electric power generations using an adaptive neural network algorithm and fuzzy DEA

    Energy Technology Data Exchange (ETDEWEB)

    Javaheri, Zahra

    2010-09-15

    Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.

  13. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Donghui Hu

    2017-01-01

    Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

  14. Climate change adaptation benefits of potential conservation partnerships.

    Science.gov (United States)

    Monahan, William B; Theobald, David M

    2018-01-01

    We evaluate the world terrestrial network of protected areas (PAs) for its partnership potential in responding to climate change. That is, if a PA engaged in collaborative, trans-boundary management of species, by investing in conservation partnerships with neighboring areas, what climate change adaptation benefits might accrue? We consider core tenets of conservation biology related to protecting large areas with high environmental heterogeneity and low climate change velocity and ask how a series of biodiversity adaptation indicators change across spatial scales encompassing potential PA and non-PA partners. Less than 1% of current world terrestrial PAs equal or exceed the size of established and successful conservation partnerships. Partnering at this scale would increase the biodiversity adaptation indicators by factors up to two orders of magnitude, compared to a null model in which each PA is isolated. Most partnership area surrounding PAs is comprised of non-PAs (70%), indicating the importance of looking beyond the current network of PAs when promoting climate change adaptation. Given monumental challenges with PA-based species conservation in the face of climate change, partnerships provide a logical and achievable strategy for helping areas adapt. Our findings identify where strategic partnering efforts in highly vulnerable areas of the world may prove critical in safeguarding biodiversity.

  15. Determination of Liquefaction Potential using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Farrokhzad, F; Choobbasti, A.J; Barari, Amin

    2011-01-01

    The authors propose an alternative general regression model based on neural networks, which enables analysis of summary data obtained by liquefaction analysis according to usual methods. For that purpose, the data from some thirty boreholes made during field investigations in Babol, in the Iranian...

  16. A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents

    Directory of Open Access Journals (Sweden)

    David Griol

    2016-01-01

    Full Text Available Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user’s intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user’s needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.

  17. A Neural Network Approach to Intention Modeling for User-Adapted Conversational Agents.

    Science.gov (United States)

    Griol, David; Callejas, Zoraida

    2016-01-01

    Spoken dialogue systems have been proposed to enable a more natural and intuitive interaction with the environment and human-computer interfaces. In this contribution, we present a framework based on neural networks that allows modeling of the user's intention during the dialogue and uses this prediction to dynamically adapt the dialogue model of the system taking into consideration the user's needs and preferences. We have evaluated our proposal to develop a user-adapted spoken dialogue system that facilitates tourist information and services and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, and the quality perceived by the users.

  18. An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    International Nuclear Information System (INIS)

    Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

    2017-01-01

    The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods. (paper)

  19. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Yudong Zhang

    2011-05-01

    Full Text Available This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM based texture features. Then, the features were reduced by principle component analysis (PCA. Finally, a two-hidden-layer forward neural network (NN was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO. K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP, adaptive BP (ABP, momentum BP (MBP, Particle Swarm Optimization (PSO, and Resilient back-propagation (RPROP methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

  20. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  1. Precision position control of servo systems using adaptive back-stepping and recurrent fuzzy neural networks

    International Nuclear Information System (INIS)

    Kim, Han Me; Kim, Jong Shik; Han, Seong Ik

    2009-01-01

    To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness

  2. A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

    Science.gov (United States)

    Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

    2018-01-01

    In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

  3. Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

    Science.gov (United States)

    Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

    2017-03-01

    In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  5. Evaluation of the maximum-likelihood adaptive neural system (MLANS) applications to noncooperative IFF

    Science.gov (United States)

    Chernick, Julian A.; Perlovsky, Leonid I.; Tye, David M.

    1994-06-01

    This paper describes applications of maximum likelihood adaptive neural system (MLANS) to the characterization of clutter in IR images and to the identification of targets. The characterization of image clutter is needed to improve target detection and to enhance the ability to compare performance of different algorithms using diverse imagery data. Enhanced unambiguous IFF is important for fratricide reduction while automatic cueing and targeting is becoming an ever increasing part of operations. We utilized MLANS which is a parametric neural network that combines optimal statistical techniques with a model-based approach. This paper shows that MLANS outperforms classical classifiers, the quadratic classifier and the nearest neighbor classifier, because on the one hand it is not limited to the usual Gaussian distribution assumption and can adapt in real time to the image clutter distribution; on the other hand MLANS learns from fewer samples and is more robust than the nearest neighbor classifiers. Future research will address uncooperative IFF using fused IR and MMW data.

  6. Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

    Directory of Open Access Journals (Sweden)

    Carlos González-Gutiérrez

    2018-01-01

    Full Text Available Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks.

  7. Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

    Directory of Open Access Journals (Sweden)

    Shu-Min Lu

    2017-01-01

    Full Text Available An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

  8. Adaptive Landing Gear: Optimum Control Strategy and Potential for Improvement

    Directory of Open Access Journals (Sweden)

    Grzegorz Mikułowski

    2009-01-01

    Full Text Available An adaptive landing gear is a landing gear (LG capable of active adaptation to particular landing conditions by means of controlled hydraulic force. The objective of the adaptive control is to mitigate the peak force transferred to the aircraft structure during touch-down, and thus to limit the structural fatigue factor. This paper investigates the ultimate limits for improvement due to various strategies of active control. Five strategies are proposed and investigated numerically using a~validated model of a real, passive landing gear as a reference. Potential for improvement is estimated statistically in terms of the mean and median (significant peak strut forces as well as in terms of the extended safe sinking velocity range. Three control strategies are verified experimentally using a laboratory test stand.

  9. Stability of bumps in piecewise smooth neural fields with nonlinear adaptation

    KAUST Repository

    Kilpatrick, Zachary P.

    2010-06-01

    We study the linear stability of stationary bumps in piecewise smooth neural fields with local negative feedback in the form of synaptic depression or spike frequency adaptation. The continuum dynamics is described in terms of a nonlocal integrodifferential equation, in which the integral kernel represents the spatial distribution of synaptic weights between populations of neurons whose mean firing rate is taken to be a Heaviside function of local activity. Discontinuities in the adaptation variable associated with a bump solution means that bump stability cannot be analyzed by constructing the Evans function for a network with a sigmoidal gain function and then taking the high-gain limit. In the case of synaptic depression, we show that linear stability can be formulated in terms of solutions to a system of pseudo-linear equations. We thus establish that sufficiently strong synaptic depression can destabilize a bump that is stable in the absence of depression. These instabilities are dominated by shift perturbations that evolve into traveling pulses. In the case of spike frequency adaptation, we show that for a wide class of perturbations the activity and adaptation variables decouple in the linear regime, thus allowing us to explicitly determine stability in terms of the spectrum of a smooth linear operator. We find that bumps are always unstable with respect to this class of perturbations, and destabilization of a bump can result in either a traveling pulse or a spatially localized breather. © 2010 Elsevier B.V. All rights reserved.

  10. Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

    Science.gov (United States)

    Wang, Ding; Liu, Derong

    2018-06-01

    The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm. (On-Line Harmonics Estimation Application

    Directory of Open Access Journals (Sweden)

    Eyad K Almaita

    2017-03-01

    Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application.  International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17

  12. Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

    International Nuclear Information System (INIS)

    Zare, Mansour; Vahdati Khaki, Jalil

    2012-01-01

    Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

  13. Quadratic adaptive algorithm for solving cardiac action potential models.

    Science.gov (United States)

    Chen, Min-Hung; Chen, Po-Yuan; Luo, Ching-Hsing

    2016-10-01

    An adaptive integration method is proposed for computing cardiac action potential models accurately and efficiently. Time steps are adaptively chosen by solving a quadratic formula involving the first and second derivatives of the membrane action potential. To improve the numerical accuracy, we devise an extremum-locator (el) function to predict the local extremum when approaching the peak amplitude of the action potential. In addition, the time step restriction (tsr) technique is designed to limit the increase in time steps, and thus prevent the membrane potential from changing abruptly. The performance of the proposed method is tested using the Luo-Rudy phase 1 (LR1), dynamic (LR2), and human O'Hara-Rudy dynamic (ORd) ventricular action potential models, and the Courtemanche atrial model incorporating a Markov sodium channel model. Numerical experiments demonstrate that the action potential generated using the proposed method is more accurate than that using the traditional Hybrid method, especially near the peak region. The traditional Hybrid method may choose large time steps near to the peak region, and sometimes causes the action potential to become distorted. In contrast, the proposed new method chooses very fine time steps in the peak region, but large time steps in the smooth region, and the profiles are smoother and closer to the reference solution. In the test on the stiff Markov ionic channel model, the Hybrid blows up if the allowable time step is set to be greater than 0.1ms. In contrast, our method can adjust the time step size automatically, and is stable. Overall, the proposed method is more accurate than and as efficient as the traditional Hybrid method, especially for the human ORd model. The proposed method shows improvement for action potentials with a non-smooth morphology, and it needs further investigation to determine whether the method is helpful during propagation of the action potential. Copyright © 2016 Elsevier Ltd. All rights

  14. Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

    Science.gov (United States)

    Fei, Juntao; Lu, Cheng

    2018-04-01

    In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

  15. An adaptive workspace hypothesis about the neural correlates of consciousness: insights from neuroscience and meditation studies.

    Science.gov (United States)

    Raffone, Antonino; Srinivasan, Narayanan

    2009-01-01

    While enormous progress has been made to identify neural correlates of consciousness (NCC), crucial NCC aspects are still very controversial. A major hurdle is the lack of an adequate definition and characterization of different aspects of conscious experience and also its relationship to attention and metacognitive processes like monitoring. In this paper, we therefore attempt to develop a unitary theoretical framework for NCC, with an interdependent characterization of endogenous attention, access consciousness, phenomenal awareness, metacognitive consciousness, and a non-referential form of unified consciousness. We advance an adaptive workspace hypothesis about the NCC based on the global workspace model emphasizing transient resonant neurodynamics and prefrontal cortex function, as well as meditation-related characterizations of conscious experiences. In this hypothesis, transient dynamic links within an adaptive coding net in prefrontal cortex, especially in anterior prefrontal cortex, and between it and the rest of the brain, in terms of ongoing intrinsic and long-range signal exchanges, flexibly regulate the interplay between endogenous attention, access consciousness, phenomenal awareness, and metacognitive consciousness processes. Such processes are established in terms of complementary aspects of an ongoing transition between context-sensitive global workspace assemblies, modulated moment-to-moment by body and environment states. Brain regions associated to momentary interoceptive and exteroceptive self-awareness, or first-person experiential perspective as emphasized in open monitoring meditation, play an important modulatory role in adaptive workspace transitions.

  16. Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

    Directory of Open Access Journals (Sweden)

    Chaojiao Sun

    2016-01-01

    Full Text Available An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

  17. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  18. Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

    Directory of Open Access Journals (Sweden)

    Padmavthi Kora

    2017-03-01

    Full Text Available The medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG. A Bundle Branch Block (BBB is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature.

  19. Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

    Directory of Open Access Journals (Sweden)

    Maode Yan

    2017-11-01

    Full Text Available This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations. In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.

  20. Bumps, breathers, and waves in a neural network with spike frequency adaptation

    International Nuclear Information System (INIS)

    Coombes, S.; Owen, M.R.

    2005-01-01

    We introduce a continuum model of neural tissue that includes the effects of spike frequency adaptation (SFA). The basic model is an integral equation for synaptic activity that depends upon nonlocal network connectivity, synaptic response, and the firing rate of a single neuron. We consider a phenomenological model of SFA via a simple state-dependent threshold firing rate function. As without SFA, Mexican-hat connectivity allows for the existence of spatially localized states (bumps). Importantly recent Evans function techniques are used to show that bumps may destabilize leading to the emergence of breathers and traveling waves. Moreover, a similar analysis for traveling pulses leads to the conditions necessary to observe a stable traveling breather. Simulations confirm our theoretical predictions and illustrate the rich behavior of this model

  1. A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability.

    Science.gov (United States)

    Agoubi, Belgacem; Dabbaghi, Radhia; Kharroubi, Adel

    2018-01-23

    Assessing groundwater vulnerability is an important procedure for sustainable water management. Various methods have been developed for effective assessment of groundwater vulnerability and protection. However, each method has its own conditions of use and, in practice; it is difficult to return the same results for the same site. The research conceptualized and developed an improved DRASTIC method using Mamdani Adaptive Neural Fuzzy Inference System (M-ANFIS-DRASTIC). DRASTIC and M-ANFIS-DRASTIC were applied in the Jorf aquifer, southeastern Tunisia, and results were compared. Results confirm that M-ANFIS-DRASTIC combined with geostatistical tools is more powerful, generated more precise vulnerability classes with very low estimation variance. Fuzzy logic has a power to produce more realistic aquifer vulnerability assessments and introduces new ways of modeling in hydrogeology using natural human language expressed by logic rules. © 2018, National Ground Water Association.

  2. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  3. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    Science.gov (United States)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

  4. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  5. Adaptive neural reward processing during anticipation and receipt of monetary rewards in mindfulness meditators.

    Science.gov (United States)

    Kirk, Ulrich; Brown, Kirk Warren; Downar, Jonathan

    2015-05-01

    Reward seeking is ubiquitous and adaptive in humans. But excessive reward seeking behavior, such as chasing monetary rewards, may lead to diminished subjective well-being. This study examined whether individuals trained in mindfulness meditation show neural evidence of lower susceptibility to monetary rewards. Seventy-eight participants (34 meditators, 44 matched controls) completed the monetary incentive delay task while undergoing functional magnetic resonance imaging. The groups performed equally on the task, but meditators showed lower neural activations in the caudate nucleus during reward anticipation, and elevated bilateral posterior insula activation during reward anticipation. Meditators also evidenced reduced activations in the ventromedial prefrontal cortex during reward receipt compared with controls. Connectivity parameters between the right caudate and bilateral anterior insula were attenuated in meditators during incentive anticipation. In summary, brain regions involved in reward processing-both during reward anticipation and receipt of reward-responded differently in mindfulness meditators than in nonmeditators, indicating that the former are less susceptible to monetary incentives. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  6. Performance assessment of electric power generations using an adaptive neural network algorithm

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Anvari, M.; Saberi, M.

    2007-01-01

    Efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making units (DMUs) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). An example using real data is presented for illustrative purposes. In the application to the power generation sector of Iran, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. Moreover, principle component analysis (PCA) is used to verify the findings of the proposed algorithm

  7. Adaptive neural network controller for the molten steel level control of strip casting processes

    International Nuclear Information System (INIS)

    Chen, Hung Yi; Huang, Shiuh Jer

    2010-01-01

    The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system's nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semi experimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional Pid controller

  8. Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

    Directory of Open Access Journals (Sweden)

    Sakyasingha eDasgupta

    2015-09-01

    Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

  9. Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

    Science.gov (United States)

    Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

    2013-10-01

    This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

  10. Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

    CERN Document Server

    Hu, H P

    2004-01-01

    In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

  11. Neural network approach for the calculation of potential coefficients in quantum mechanics

    Science.gov (United States)

    Ossandón, Sebastián; Reyes, Camilo; Cumsille, Patricio; Reyes, Carlos M.

    2017-05-01

    A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss.

  12. The potential of neural transplantation for brain repair and regeneration following traumatic brain injury

    Institute of Scientific and Technical Information of China (English)

    Dong Sun

    2016-01-01

    Traumatic brain injury is a major health problem worldwide. Currently, there is no effective treatment to improve neural structural repair and functional recovery of patients in the clinic. Cell transplantation is a potential strategy to repair and regenerate the injured brain. This review article summarized recent de-velopment in cell transplantation studies for post-traumatic brain injury brain repair with varying types of cell sources. It also discussed the potential of neural transplantation to repair/promote recovery of the injured brain following traumatic brain injury.

  13. Potential usefulness of an artificial neural network for assessing ventricular size

    International Nuclear Information System (INIS)

    Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

    1995-01-01

    An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

  14. A Neural-Network-Based Nonlinear Adaptive State-Observer for Pressurized Water Reactors

    Directory of Open Access Journals (Sweden)

    Zhe Dong

    2013-10-01

    Full Text Available Although there have been some severe nuclear accidents such as Three Mile Island (USA, Chernobyl (Ukraine and Fukushima (Japan, nuclear fission energy is still a source of clean energy that can substitute for fossil fuels in a centralized way and in a great amount with commercial availability and economic competitiveness. Since the pressurized water reactor (PWR is the most widely used nuclear fission reactor, its safe, stable and efficient operation is meaningful to the current rebirth of the nuclear fission energy industry. Power-level regulation is an important technique which can deeply affect the operation stability and efficiency of PWRs. Compared with the classical power-level controllers, the advanced power-level regulators could strengthen both the closed-loop stability and control performance by feeding back the internal state-variables. However, not all of the internal state variables of a PWR can be obtained directly by measurements. To implement advanced PWR power-level control law, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with parameters varying with power-level, fuel burnup, xenon isotope production, control rod worth and etc., it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multi-layer perceptron (MLP neural network, an MLP-based nonlinear adaptive observer is given for PWRs. Based upon Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e., the nuclear heating reactor (NHR, and numerical simulation results not only verify its feasibility but also give the relationship between the observation performance and observer parameters.

  15. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  16. Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments.

    Science.gov (United States)

    Fang, Shih-Hau; Lin, Tsung-Nan

    2008-11-01

    This brief paper presents a novel localization algorithm, named discriminant-adaptive neural network (DANN), which takes the received signal strength (RSS) from the access points (APs) as inputs to infer the client position in the wireless local area network (LAN) environment. We extract the useful information into discriminative components (DCs) for network learning. The nonlinear relationship between RSS and the position is then accurately constructed by incrementally inserting the DCs and recursively updating the weightings in the network until no further improvement is required. Our localization system is developed in a real-world wireless LAN WLAN environment, where the realistic RSS measurement is collected. We implement the traditional approaches on the same test bed, including weighted kappa-nearest neighbor (WKNN), maximum likelihood (ML), and multilayer perceptron (MLP), and compare the results. The experimental results indicate that the proposed algorithm is much higher in accuracy compared with other examined techniques. The improvement can be attributed to that only the useful information is efficiently extracted for positioning while the redundant information is regarded as noise and discarded. Finally, the analysis shows that our network intelligently accomplishes learning while the inserted DCs provide sufficient information.

  17. Adaptive eye-gaze tracking using neural-network-based user profiles to assist people with motor disability.

    Science.gov (United States)

    Sesin, Anaelis; Adjouadi, Malek; Cabrerizo, Mercedes; Ayala, Melvin; Barreto, Armando

    2008-01-01

    This study developed an adaptive real-time human-computer interface (HCI) that serves as an assistive technology tool for people with severe motor disability. The proposed HCI design uses eye gaze as the primary computer input device. Controlling the mouse cursor with raw eye coordinates results in sporadic motion of the pointer because of the saccadic nature of the eye. Even though eye movements are subtle and completely imperceptible under normal circumstances, they considerably affect the accuracy of an eye-gaze-based HCI. The proposed HCI system is novel because it adapts to each specific user's different and potentially changing jitter characteristics through the configuration and training of an artificial neural network (ANN) that is structured to minimize the mouse jitter. This task is based on feeding the ANN a user's initially recorded eye-gaze behavior through a short training session. The ANN finds the relationship between the gaze coordinates and the mouse cursor position based on the multilayer perceptron model. An embedded graphical interface is used during the training session to generate user profiles that make up these unique ANN configurations. The results with 12 subjects in test 1, which involved following a moving target, showed an average jitter reduction of 35%; the results with 9 subjects in test 2, which involved following the contour of a square object, showed an average jitter reduction of 53%. For both results, the outcomes led to trajectories that were significantly smoother and apt at reaching fixed or moving targets with relative ease and within a 5% error margin or deviation from desired trajectories. The positive effects of such jitter reduction are presented graphically for visual appreciation.

  18. A neural-network potential through charge equilibration for WS2: From clusters to sheets

    Science.gov (United States)

    Hafizi, Roohollah; Ghasemi, S. Alireza; Hashemifar, S. Javad; Akbarzadeh, Hadi

    2017-12-01

    In the present work, we use a machine learning method to construct a high-dimensional potential for tungsten disulfide using a charge equilibration neural-network technique. A training set of stoichiometric WS2 clusters is prepared in the framework of density functional theory. After training the neural-network potential, the reliability and transferability of the potential are verified by performing a crystal structure search on bulk phases of WS2 and by plotting energy-area curves of two different monolayers. Then, we use the potential to investigate various triangular nano-clusters and nanotubes of WS2. In the case of nano-structures, we argue that 2H atomic configurations with sulfur rich edges are thermodynamically more stable than the other investigated configurations. We also studied a number of WS2 nanotubes which revealed that 1T tubes with armchair chirality exhibit lower bending stiffness.

  19. Neural substrate of the late positive potential in emotional processing

    Science.gov (United States)

    Liu, Yuelu; Huang, Haiqing; McGinnis, Menton; Keil, Andreas; Ding, Mingzhou

    2012-01-01

    The late positive potential (LPP) is a reliable electrophysiological index of emotional perception in humans. Despite years of research the brain structures that contribute to the generation and modulation of LPP are not well understood. Recording EEG and fMRI simultaneously, and applying a recently proposed single-trial ERP analysis method, we addressed the problem by correlating the single-trial LPP amplitude evoked by affective pictures with the blood-oxygen-level-dependent (BOLD) activity. Three results were found. First, relative to neutral pictures, pleasant and unpleasant pictures elicited enhanced LPP, as well as heightened BOLD activity in both visual cortices and emotion-processing structures such as amygdala and prefrontal cortex, consistent with previous findings. Second, the LPP amplitude across three picture categories was significantly correlated with BOLD activity in visual cortices, temporal cortices, amygdala, orbitofrontal cortex, and insula. Third, within each picture category, LPP-BOLD coupling revealed category-specific differences. For pleasant pictures, the LPP amplitude was coupled with BOLD in occipitotemporal junction, medial prefrontal cortex, amygdala, and precuneus, whereas for unpleasant pictures, significant LPP-BOLD correlation was observed in ventrolateral prefrontal cortex, insula, and posterior cingulate cortex. These results suggest that LPP is generated and modulated by an extensive brain network comprised of both cortical and subcortical structures associated with visual and emotional processing and the degree of contribution by each of these structures to the LPP modulation is valence-specific. PMID:23077042

  20. A novel method for extraction of neural response from single channel cochlear implant auditory evoked potentials.

    Science.gov (United States)

    Sinkiewicz, Daniel; Friesen, Lendra; Ghoraani, Behnaz

    2017-02-01

    Cortical auditory evoked potentials (CAEP) are used to evaluate cochlear implant (CI) patient auditory pathways, but the CI device produces an electrical artifact, which obscures the relevant information in the neural response. Currently there are multiple methods, which attempt to recover the neural response from the contaminated CAEP, but there is no gold standard, which can quantitatively confirm the effectiveness of these methods. To address this crucial shortcoming, we develop a wavelet-based method to quantify the amount of artifact energy in the neural response. In addition, a novel technique for extracting the neural response from single channel CAEPs is proposed. The new method uses matching pursuit (MP) based feature extraction to represent the contaminated CAEP in a feature space, and support vector machines (SVM) to classify the components as normal hearing (NH) or artifact. The NH components are combined to recover the neural response without artifact energy, as verified using the evaluation tool. Although it needs some further evaluation, this approach is a promising method of electrical artifact removal from CAEPs. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

  1. Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad S. Islam

    2017-01-01

    Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.

  2. Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

    OpenAIRE

    GÖKCE, Kürşad; UYAROĞLU, Yılmaz

    2013-01-01

    This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.

  3. PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

    OpenAIRE

    Cancelier, A.; Claumann, C. A.; Bolzan, A.; Machado, R. A. F.

    2016-01-01

    Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, ...

  4. Adaptation decision-making in the Nordic Countries: assessing the potential for joint action

    DEFF Research Database (Denmark)

    Juhola, Sirkku; Goodsite, Michael Evan; Davis, Marion

    2014-01-01

    on the issue. This paper explores the potential for Nordic cooperation on adaptation; specifically, for the development of a regional adaptation strategy. In particular, it addresses two questions (1) What is the current state of adaptation in the Nordic countries? and (2) What are the potential benefits...

  5. A novel neural-net-based nonlinear adaptive control and application to the cross-direction deviations control of a polymer film spread line

    International Nuclear Information System (INIS)

    Chen Zengqiang; Li Xiang; Liu Zhongxin; Yuan Zhuzhi

    2008-01-01

    A novel neural adaptive controller is presented to effectively control multivariable nonlinear systems. The proposed neural controller has been successfully applied to the cross-direction deviation control system of a polymer film spread line, whose good performance has been verified with real-time running results

  6. Improvement of the Hopfield Neural Network by MC-Adaptation Rule

    Science.gov (United States)

    Zhou, Zhen; Zhao, Hong

    2006-06-01

    We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

  7. Adaptive Learning and Thinking Style to Improve E-Learning Environment Using Neural Network (ALTENN) Model

    OpenAIRE

    Dagez, Hanan Ettaher; Ambarka, Ali Elghali

    2015-01-01

     In recent years we have witnessed an increasingly heightened awareness of the potential benefits of adaptively in e-learning. This has been mainly driven by the realization that the ideal of individualized learning (i.e., learning tailored to the specific requirements and preferences of the individual) cannot be achieved, especially at a “massive” scale, using traditional approaches. In e-learning when the learning style of the student is not compatible with the teaching style of the teacher...

  8. Hybrid feedback feedforward: An efficient design of adaptive neural network control.

    Science.gov (United States)

    Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

    2016-04-01

    This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

    Science.gov (United States)

    Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

    2015-12-01

    Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

  10. A neural algorithm for the non-uniform and adaptive sampling of biomedical data.

    Science.gov (United States)

    Mesin, Luca

    2016-04-01

    Body sensors are finding increasing applications in the self-monitoring for health-care and in the remote surveillance of sensitive people. The physiological data to be sampled can be non-stationary, with bursts of high amplitude and frequency content providing most information. Such data could be sampled efficiently with a non-uniform schedule that increases the sampling rate only during activity bursts. A real time and adaptive algorithm is proposed to select the sampling rate, in order to reduce the number of measured samples, but still recording the main information. The algorithm is based on a neural network which predicts the subsequent samples and their uncertainties, requiring a measurement only when the risk of the prediction is larger than a selectable threshold. Four examples of application to biomedical data are discussed: electromyogram, electrocardiogram, electroencephalogram, and body acceleration. Sampling rates are reduced under the Nyquist limit, still preserving an accurate representation of the data and of their power spectral densities (PSD). For example, sampling at 60% of the Nyquist frequency, the percentage average rectified errors in estimating the signals are on the order of 10% and the PSD is fairly represented, until the highest frequencies. The method outperforms both uniform sampling and compressive sensing applied to the same data. The discussed method allows to go beyond Nyquist limit, still preserving the information content of non-stationary biomedical signals. It could find applications in body sensor networks to lower the number of wireless communications (saving sensor power) and to reduce the occupation of memory. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. An Adaptive Landscape Classification Procedure using Geoinformatics and Artificial Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Coleman, Andre Michael [Vrije Univ., Amsterdam (Netherlands)

    2008-06-01

    The Adaptive Landscape Classification Procedure (ALCP), which links the advanced geospatial analysis capabilities of Geographic Information Systems (GISs) and Artificial Neural Networks (ANNs) and particularly Self-Organizing Maps (SOMs), is proposed as a method for establishing and reducing complex data relationships. Its adaptive and evolutionary capability is evaluated for situations where varying types of data can be combined to address different prediction and/or management needs such as hydrologic response, water quality, aquatic habitat, groundwater recharge, land use, instrumentation placement, and forecast scenarios. The research presented here documents and presents favorable results of a procedure that aims to be a powerful and flexible spatial data classifier that fuses the strengths of geoinformatics and the intelligence of SOMs to provide data patterns and spatial information for environmental managers and researchers. This research shows how evaluation and analysis of spatial and/or temporal patterns in the landscape can provide insight into complex ecological, hydrological, climatic, and other natural and anthropogenic-influenced processes. Certainly, environmental management and research within heterogeneous watersheds provide challenges for consistent evaluation and understanding of system functions. For instance, watersheds over a range of scales are likely to exhibit varying levels of diversity in their characteristics of climate, hydrology, physiography, ecology, and anthropogenic influence. Furthermore, it has become evident that understanding and analyzing these diverse systems can be difficult not only because of varying natural characteristics, but also because of the availability, quality, and variability of spatial and temporal data. Developments in geospatial technologies, however, are providing a wide range of relevant data, and in many cases, at a high temporal and spatial resolution. Such data resources can take the form of high

  12. Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

    Directory of Open Access Journals (Sweden)

    Ali Unluturk

    2017-03-01

    Full Text Available In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.

  13. Exploring the potential of climate adaptive building shells

    NARCIS (Netherlands)

    Loonen, R.C.G.M.; Trcka, M.; Hensen, J.L.M.

    2011-01-01

    Building shells with adaptive, rather than static properties, intuitively offer opportunities for both energy savings and comfort enhancements. Progress in this field is characterized by fragmented developments, and the most effective type of climate adaptive building shell (CABS) behaviour is still

  14. An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2016-01-01

    expensive algorithms. We present a novel bioinspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural...

  15. Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori.

    Science.gov (United States)

    Chen, Weisheng; Ge, Shuzhi Sam; Wu, Jian; Gong, Maoguo

    2015-09-01

    This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

  16. Convolutional neural networks for event-related potential detection: impact of the architecture.

    Science.gov (United States)

    Cecotti, H

    2017-07-01

    The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

  17. Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials.

    Science.gov (United States)

    Jin, Jia; Yu, Liping; Ma, Qingguo

    2015-01-01

    Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.

  18. Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

    Science.gov (United States)

    Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

    2017-08-29

    This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

  19. Synthesis of a novel adaptive wavelet optimized neural cascaded steam blow-off control system for a nuclear power plant

    International Nuclear Information System (INIS)

    Malik, A.H.; Memon, A.A.; Arshad, F.

    2013-01-01

    Blow-Off System Controller (MIMO AWNN-SBOSC) is designed based on real time dynamic parametric plant data of steam blow-off system with conventional Single-Input Multi-Output Proportional plus Integral plus Derivative Controller (SIMO PIDC). The proposed MIMO AWANN-SBOSC is designed using three Multi-Input Single-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Controllers (MISO AWNN-SBOSC). The hidden layer of each MISO AWNN-SBOSC is formulated using Mother Wavelet Transforms (MWT). Using nonlinear dynamic neural data of designed MIMO AWNN-SBOSC, a Multi-Input Multi-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Model (MIMO AWNN-SBOSM) is developed in cascaded mode. MIMO AWNN-SBOSM is designed using two MISO AWNN-SBOSM. All training, testing and validation of MIMO AWNN-SBOSC and MIMO AWNN-SBOSM are carried out in MA TLAB while all simulation experiments are performed in Visual C. The results of the new design is evaluated against conventional controller based measured data and found robust, fast and much better in performance. (author)

  20. Adaptation of the Neural Network Recognition System of the Helicopter on Its Acoustic Radiation to the Flight Speed

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2015-01-01

    Full Text Available The article concerns the adaptation of a neural tract that recognizes a helicopter from the aerodynamic and ground objects by its acoustic radiation to the helicopter flight speed. It uses non-centered informative signs-indications of estimating signal spectra, which correspond to the local extremes (maximums and minimums of the power spectrum of input signal and have the greatest information when differentiating the helicopter signals from those of tracked vehicles. The article gives justification to the principle of the neural network (NN adaptation and adaptation block structure, which solves problems of blade passage frequency estimation when capturing the object and track it when tracking a target, as well as forming a signal to control the resonant filter parameters of the selection block of informative signs. To create the discriminatory characteristics of the discriminator are used autoregressive statistical characteristics of the quadrature components of signal, obtained through the discrete Hilbert Converter (DGC that perforMathematical modeling of the tracking meter using the helicopter signals obtained in real conditions is performed. The article gives estimates of the tracking parameter when using a tracking meter with DGC by sequential records of realized acoustic noise of the helicopter. It also shows a block-diagram of the adaptive NN. The scientific novelty of the work is that providing the invariance of used informative sign, the counts of local extremes of power spectral density (PSD to changes in the helicopter flight speed is reached due to adding the NN structure and adaptation block, which is implemented as a meter to track the apparent passage frequency of the helicopter rotor blades using its relationship with a function of the autoregressive acoustic signal of the helicopter.Specialized literature proposes solutions based on the use of training classifiers with different parametric methods of spectral representations

  1. Potential Roles of Dental Pulp Stem Cells in Neural Regeneration and Repair

    Science.gov (United States)

    Luo, Lihua; Wang, Xiaoyan; Key, Brian; Lee, Bae Hoon

    2018-01-01

    This review summarizes current advances in dental pulp stem cells (DPSCs) and their potential applications in the nervous diseases. Injured adult mammalian nervous system has a limited regenerative capacity due to an insufficient pool of precursor cells in both central and peripheral nervous systems. Nerve growth is also constrained by inhibitory factors (associated with central myelin) and barrier tissues (glial scarring). Stem cells, possessing the capacity of self-renewal and multicellular differentiation, promise new therapeutic strategies for overcoming these impediments to neural regeneration. Dental pulp stem cells (DPSCs) derive from a cranial neural crest lineage, retain a remarkable potential for neuronal differentiation, and additionally express multiple factors that are suitable for neuronal and axonal regeneration. DPSCs can also express immunomodulatory factors that stimulate formation of blood vessels and enhance regeneration and repair of injured nerve. These unique properties together with their ready accessibility make DPSCs an attractive cell source for tissue engineering in injured and diseased nervous systems. In this review, we interrogate the neuronal differentiation potential as well as the neuroprotective, neurotrophic, angiogenic, and immunomodulatory properties of DPSCs and its application in the injured nervous system. Taken together, DPSCs are an ideal stem cell resource for therapeutic approaches to neural repair and regeneration in nerve diseases. PMID:29853908

  2. Comparison of Artificial Neural Networks and GIS Based Solar Analysis for Solar Potential Estimation

    Science.gov (United States)

    Konakoǧlu, Berkant; Usta, Ziya; Cömert, Çetin; Gökalp, Ertan

    2016-04-01

    Nowadays, estimation of solar potential plays an important role in planning process for sustainable cities. The use of solar panels, which produces electricity directly from the sun, has become popular in accordance with developing technologies. Since the use of solar panels enables the users to decrease costs and increase yields, the use of solar panels will be more popular in the future. Production of electricity is not convenient for all circumstances. Shading effects, massive clouds and rainy weather are some factors that directly affect the production of electricity from solar energy. Hence, before the installation of solar panels, it is crucial to conduct spatial analysis and estimate the solar potential of the place that the solar panel will be installed. There are several approaches to determine the solar potential. Examination of the applications in the literature reveals that the applications conducted for determining the solar potential are divided into two main categories. Solar potential is estimated either by using artificial neural network approach in which statistical parameters such as the duration of sun shine, number of clear days, solar radiation etc. are used, or by spatial analysis conducted in GIS approaches in which spatial parameters such as, latitude, longitude, slope, aspect etc. are used. In the literature, there are several studies that use both approaches but the literature lacks of a study related to the comparison of these approaches. In this study, Karadeniz Technical University campus has been selected as study area. Monthly average values of the number of clear sky days, air temperature, atmospheric pressure, relative humidity, sunshine duration and solar radiation parameters obtained for the years between 2005 and 2015 will be used to perform artificial neural network analysis to estimate the solar potential of the study area. The solar potential will also be estimated by using GIS-based solar analysis modules. The results of

  3. Neural differentiation potential of human bone marrow-derived mesenchymal stromal cells: misleading marker gene expression

    Directory of Open Access Journals (Sweden)

    Montzka Katrin

    2009-03-01

    Full Text Available Abstract Background In contrast to pluripotent embryonic stem cells, adult stem cells have been considered to be multipotent, being somewhat more restricted in their differentiation capacity and only giving rise to cell types related to their tissue of origin. Several studies, however, have reported that bone marrow-derived mesenchymal stromal cells (MSCs are capable of transdifferentiating to neural cell types, effectively crossing normal lineage restriction boundaries. Such reports have been based on the detection of neural-related proteins by the differentiated MSCs. In order to assess the potential of human adult MSCs to undergo true differentiation to a neural lineage and to determine the degree of homogeneity between donor samples, we have used RT-PCR and immunocytochemistry to investigate the basal expression of a range of neural related mRNAs and proteins in populations of non-differentiated MSCs obtained from 4 donors. Results The expression analysis revealed that several of the commonly used marker genes from other studies like nestin, Enolase2 and microtubule associated protein 1b (MAP1b are already expressed by undifferentiated human MSCs. Furthermore, mRNA for some of the neural-related transcription factors, e.g. Engrailed-1 and Nurr1 were also strongly expressed. However, several other neural-related mRNAs (e.g. DRD2, enolase2, NFL and MBP could be identified, but not in all donor samples. Similarly, synaptic vesicle-related mRNA, STX1A could only be detected in 2 of the 4 undifferentiated donor hMSC samples. More significantly, each donor sample revealed a unique expression pattern, demonstrating a significant variation of marker expression. Conclusion The present study highlights the existence of an inter-donor variability of expression of neural-related markers in human MSC samples that has not previously been described. This donor-related heterogeneity might influence the reproducibility of transdifferentiation protocols as

  4. Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

    Science.gov (United States)

    Si, Wenjie; Dong, Xunde; Yang, Feifei

    2018-03-01

    This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Using neural networks to represent potential surfaces as sums of products.

    Science.gov (United States)

    Manzhos, Sergei; Carrington, Tucker

    2006-11-21

    By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.

  6. Modelling of solar energy potential in Nigeria using an artificial neural network model

    International Nuclear Information System (INIS)

    Fadare, D.A.

    2009-01-01

    In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Nigeria (lat. 4-14 o N, log. 2-15 o E) was developed. Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using neural toolbox for MATLAB. Geographical and meteorological data of 195 cities in Nigeria for period of 10 years (1983-1993) from the NASA geo-satellite database were used for the training and testing the network. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, mean temperature, and relative humidity) were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 90%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available. The predicted solar radiation values from the model were given in form of monthly maps. The monthly mean solar radiation potential in northern and southern regions ranged from 7.01-5.62 to 5.43-3.54 kW h/m 2 day, respectively. A graphical user interface (GUI) was developed for the application of the model. The model can be used easily for estimation of solar radiation for preliminary design of solar applications.

  7. Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

    Science.gov (United States)

    Sengupta, Biswa; Laughlin, Simon Barry; Niven, Jeremy Edward

    2014-01-01

    Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation. PMID:24465197

  8. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jun; Jiang, Bin; Guo, Hua, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States)

    2013-11-28

    A rigorous, general, and simple method to fit global and permutation invariant potential energy surfaces (PESs) using neural networks (NNs) is discussed. This so-called permutation invariant polynomial neural network (PIP-NN) method imposes permutation symmetry by using in its input a set of symmetry functions based on PIPs. For systems with more than three atoms, it is shown that the number of symmetry functions in the input vector needs to be larger than the number of internal coordinates in order to include both the primary and secondary invariant polynomials. This PIP-NN method is successfully demonstrated in three atom-triatomic reactive systems, resulting in full-dimensional global PESs with average errors on the order of meV. These PESs are used in full-dimensional quantum dynamical calculations.

  9. Adaptive Integration and Optimization of Automated and Neural Processing Systems - Establishing Neural and Behavioral Benchmarks of Optimized Performance

    Science.gov (United States)

    2012-07-01

    trial basis. Here subjects were asked to detect potential threats (vehicles, people , etc.) over a wide field-of-view. Regions with potential threats...military alphabet). They 27 were instructed to push a second button if they heard specific target words or call signs (e.g., “november,” “ zulu

  10. An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: a simulation study.

    Science.gov (United States)

    Padhi, Radhakant; Bhardhwaj, Jayender R

    2009-06-01

    An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

  11. Studies on the phase diagram of boron employing a neural network potential

    Energy Technology Data Exchange (ETDEWEB)

    Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum (Germany); Parrinello, Michele [Department of Chemistry and Applied Biosciences, ETH Zuerich (Switzerland)

    2009-07-01

    The crystalline phases of elemental boron have a structural complexity unique in the periodic table. The complex connection pattern of the icosahedral building blocks forms a formidable challenge for the construction of accurate but efficient potentials. We present a high-dimensional neural network potential for boron, which is based on first-principles calculations and can be systematically improved. The potential is several orders of magnitude faster to evaluate than the underlying density-functional theory calculations and allows to perform long molecular dynamics and metadynamics simulations of large system. By a stepwise refinement of the potential and an application of the potential in metadynamics simulations we show that starting from random atomic positions the structure of {alpha}-boron is predicted in agreement with experiment. Further, pressure-induced phase transitions of {alpha}-boron are discussed.

  12. The potential of Norwegian ecosystems for climate mitigation and adaptation

    Energy Technology Data Exchange (ETDEWEB)

    Rusch, Graciela M.

    2012-07-01

    higher soil erosion. These challenges will likely be of more concern in the future according to the projected changes in the climate. The impacts of human activities on the capacity of ecosystems to provide services are emphasized as well as the potential benefits that can be obtained both by incorporating the multiple values of nature into planning and by improving the management of live systems. There is a strong weight of examples from boreal forest because of the extent and economic importance of this nature type in Norway, and because decisions about land-use and forest management have important consequences for the provision of many benefits. There are also examples from floodplains and riparian ecosystems because of their value in water flow and flood control. Green infrastructure is presented as a complementary resource to other proposed climate change adaptation measures. Finally, some additional benefits are highlighted and used as examples of trade-offs and synergies among the multiple services associated with climate change, underscoring the value of the ecosystem service framework to inform decisionmaking.(Author)

  13. Rethinking Energy in Parkinsonian Motor Symptoms: A Potential Role for Neural Metabolic Deficits

    Directory of Open Access Journals (Sweden)

    Shinichi eAmano

    2015-01-01

    Full Text Available Parkinson’s disease (PD is characterized as a chronic and progressive neurodegenerative disorder that results in a variety of debilitating symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. Research spanning several decades has emphasized basal ganglia dysfunction, predominantly resulting from dopaminergic cell loss, as the primarily cause of the aforementioned parkinsonian features. But, why those particular features manifest themselves remains an enigma. The goal of this paper is to develop a theoretical framework that parkinsonian motor features are behavioral consequence of a long-term adaptation to their inability (inflexibility or lack of capacity to meet energetic demands, due to neural metabolic deficits arising from mitochondrial dysfunction associated with PD. Here, we discuss neurophysiological changes that are generally associated with PD, such as selective degeneration of dopaminergic neurons in the substantia nigra pars compacta, in conjunction with metabolic and mitochondrial dysfunction. We then characterize the cardinal motor symptoms of PD, bradykinesia, resting tremor, rigidity and gait disturbance, reviewing literature to demonstrate how these motor patterns are actually energy efficient from a metabolic perspective. We will also develop three testable hypotheses: (1 neural metabolic deficits precede the increased rate of neurodegeneration and onset of behavioral symptoms in PD, (2 motor behavior of persons with PD are more sensitive to changes in metabolic/bioenergetic state, and (3 improvement of metabolic function could lead to better motor performance in persons with PD. These hypotheses are designed to introduce a novel viewpoint that can elucidate the connections between metabolic, neural and motor function in PD.

  14. An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.

    Science.gov (United States)

    Hazrati, Mehrnaz Kh; Erfanian, Abbas

    2008-01-01

    This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.

  15. Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small

  16. An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

    International Nuclear Information System (INIS)

    Mieloszyk, Magdalena; Skarbek, Lukasz; Ostachowicz, Wieslaw; Krawczuk, Marek

    2011-01-01

    This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS (registered) and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

  17. Automatic change detection in vision: Adaptation, memory mismatch, or both? II: Oddball and adaptation effects on event-related potentials.

    Science.gov (United States)

    Bodnár, Flóra; File, Domonkos; Sulykos, István; Kecskés-Kovács, Krisztina; Czigler, István

    2017-11-01

    In this study we compared the event-related potentials (ERPs) obtained in two different paradigms: a passive visual oddball paradigm and an adaptation paradigm. The aim of the study was to investigate the relation between the effects of activity decrease following an adaptor (stimulus-specific adaptation) and the effects of an infrequent stimulus within sequences of frequent ones. In Experiment 1, participants were presented with different line textures. The frequent (standard) and rare (deviant) texture elements differed in their orientation. In Experiment 2, windmill pattern stimuli were presented in which the number of vanes differentiated the deviant and standard stimuli. In Experiment 1 the ERP differences elicited between the oddball deviant and the standard were similar to the differences between the ERPs to the nonadapted and adapted stimuli in the adaptation paradigm. In both paradigms the differences appeared as a posterior negativity with the latency of 120-140 ms. This finding demonstrates that the representation of a sequential rule (successive presentation of the standard) and the violation of this rule are not necessary for deviancy effects to emerge. In Experiment 2 (windmill pattern), in the oddball paradigm the difference potentials appeared as a long-lasting negativity. In the adaptation condition, the later part of this negativity (after 200 ms) was absent. We identified the later part of the oddball difference potential as the genuine visual mismatch negativity-that is, an ERP correlate of sequence violations. The latencies of the difference potentials (deviant minus standard) and the endogenous components (P1 and N1) diverged; therefore, the adaptation of these particular ERP components cannot explain the deviancy effect. Accordingly, the sources contributing to the standard-versus-deviant modulations differed from those related to visual adaptation; that is, they generated distinct ERP components.

  18. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data

    International Nuclear Information System (INIS)

    Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet

    2004-01-01

    Turkey is located at the Mediterranean at 36 deg. and 42 deg. N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m 2 day, and the total yearly radiation period is ∼2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goeztepe, Van, Izmir, Denizli, Sanliurfa, Mersin, Adana, Gaziantep, Aydin, Bursa, Diyarbakir, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R 2 values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the best solar

  19. Sorting of pistachio nuts using image processing techniques and an adaptive neural-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    A. R Abdollahnejad Barough

    2016-04-01

    . Finally, a total amount of the second moment (m2 and matrix vectors of image were selected as features. Features and rules produced from decision tree fed into an Adaptable Neuro-fuzzy Inference System (ANFIS. ANFIS provides a neural network based on Fuzzy Inference System (FIS can produce appropriate output corresponding input patterns. Results and Discussion: The proposed model was trained and tested inside ANFIS Editor of the MATLAB software. 300 images, including closed shell, pithy and empty pistachio were selected for training and testing. This network uses 200 data related to these two features and were trained over 200 courses, the accuracy of the result was 95.8%. 100 image have been used to test network over 40 courses with accuracy 97%. The time for the training and testing steps are 0.73 and 0.31 seconds, respectively, and the time to choose the features and rules was 2.1 seconds. Conclusions: In this study, a model was introduced to sort non- split nuts, blank nuts and filled nuts pistachios. Evaluation of training and testing, shows that the model has the ability to classify different types of nuts with high precision. In the previously proposed methods, merely non-split and split pistachio nuts were sorted and being filled or blank nuts is unrecognizable. Nevertheless, accuracy of the mentioned method is 95.56 percent. As well as, other method sorted non-split and split pistachio nuts with an accuracy of 98% and 85% respectively for training and testing steps. The model proposed in this study is better than the other methods and it is encouraging for the improvement and development of the model.

  20. Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

    Science.gov (United States)

    Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

    2014-06-01

    This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

  1. An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

    Science.gov (United States)

    Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

    2016-05-01

    Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  2. The specificity of neural responses to music and their relation to voice processing: an fMRI-adaptation study.

    Science.gov (United States)

    Armony, Jorge L; Aubé, William; Angulo-Perkins, Arafat; Peretz, Isabelle; Concha, Luis

    2015-04-23

    Several studies have identified, using functional magnetic resonance imaging (fMRI), a region within the superior temporal gyrus that preferentially responds to musical stimuli. However, in most cases, significant responses to other complex stimuli, particularly human voice, were also observed. Thus, it remains unknown if the same neurons respond to both stimulus types, albeit with different strengths, or whether the responses observed with fMRI are generated by distinct, overlapping neural populations. To address this question, we conducted an fMRI experiment in which short music excerpts and human vocalizations were presented in a pseudo-random order. Critically, we performed an adaptation-based analysis in which responses to the stimuli were analyzed taking into account the category of the preceding stimulus. Our results confirm the presence of a region in the anterior STG that responds more strongly to music than voice. Moreover, we found a music-specific adaptation effect in this area, consistent with the existence of music-preferred neurons. Lack of differences between musicians and non-musicians argues against an expertise effect. These findings provide further support for neural separability between music and speech within the temporal lobe. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  4. Highly Adaptive Primary Mirror Having Embedded Actuators, Sensors, and Neural Control, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — Xinetics has demonstrated the technology required to fabricate a self-compensating highly adaptive silicon carbide primary mirror system having embedded actuators,...

  5. Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator

    Science.gov (United States)

    Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung

    2005-12-01

    The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

  6. An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.

    Science.gov (United States)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities-0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking.

  7. Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kolb, Brian [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Zhao, Bin; Guo, Hua, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Li, Jun [School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331 (China); Jiang, Bin [Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China)

    2016-06-14

    The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H{sub 2} → H{sub 2} + H, H + H{sub 2}O → H{sub 2} + OH, and H + CH{sub 4} → H{sub 2} + CH{sub 3}. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.

  8. Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks

    International Nuclear Information System (INIS)

    Kolb, Brian; Zhao, Bin; Guo, Hua; Li, Jun; Jiang, Bin

    2016-01-01

    The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H 2 → H 2 + H, H + H 2 O → H 2 + OH, and H + CH 4 → H 2 + CH 3 . A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.

  9. Non-resonant energy harvesting via an adaptive bistable potential

    International Nuclear Information System (INIS)

    Hosseinloo, Ashkan Haji; Turitsyn, Konstantin

    2016-01-01

    Narrow bandwidth and easy detuning, inefficiency in broadband and non-stationary excitations, and difficulties in matching a linear harvester’s resonance frequency to low-frequency excitations at small scales, have convinced researchers to investigate nonlinear, and in particular bistable, energy harvesters in recent years. However, bistable harvesters suffer from co-existing low and high energy orbits, and sensitivity to initial conditions, and have recently been proven inefficient when subjected to many real-world random and non-stationary excitations. Here, we propose a novel non-resonant buy-low-sell-high strategy that can significantly improve the harvester’s effectiveness at low frequencies in a much more robust fashion. This strategy could be realized by a passive adaptive bistable system. Simulation results confirm the high effectiveness of the adaptive bistable system following a buy-low-sell-high logic when subjected to harmonic and random non-stationary walking excitations compared to its conventional bistable and linear counterparts. (paper)

  10. Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

    International Nuclear Information System (INIS)

    Luo, Shaohua; Wu, Songli; Gao, Ruizhen

    2015-01-01

    This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation

  11. Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

    Science.gov (United States)

    Luo, Shaohua; Wu, Songli; Gao, Ruizhen

    2015-07-01

    This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

  12. Rapid and Objective Assessment of Neural Function in Autism Spectrum Disorder Using Transient Visual Evoked Potentials.

    Directory of Open Access Journals (Sweden)

    Paige M Siper

    Full Text Available There is a critical need to identify biomarkers and objective outcome measures that can be used to understand underlying neural mechanisms in autism spectrum disorder (ASD. Visual evoked potentials (VEPs offer a noninvasive technique to evaluate the functional integrity of neural mechanisms, specifically visual pathways, while probing for disease pathophysiology.Transient VEPs (tVEPs were obtained from 96 unmedicated children, including 37 children with ASD, 36 typically developing (TD children, and 23 unaffected siblings (SIBS. A conventional contrast-reversing checkerboard condition was compared to a novel short-duration condition, which was developed to enable objective data collection from severely affected populations who are often excluded from electroencephalographic (EEG studies.Children with ASD showed significantly smaller amplitudes compared to TD children at two of the earliest critical VEP components, P60-N75 and N75-P100. SIBS showed intermediate responses relative to ASD and TD groups. There were no group differences in response latency. Frequency band analyses indicated significantly weaker responses for the ASD group in bands encompassing gamma-wave activity. Ninety-two percent of children with ASD were able to complete the short-duration condition compared to 68% for the standard condition.The current study establishes the utility of a short-duration tVEP test for use in children at varying levels of functioning and describes neural abnormalities in children with idiopathic ASD. Implications for excitatory/inhibitory balance as well as the potential application of VEP for use in clinical trials are discussed.

  13. From sensation to percept: the neural signature of auditory event-related potentials.

    Science.gov (United States)

    Joos, Kathleen; Gilles, Annick; Van de Heyning, Paul; De Ridder, Dirk; Vanneste, Sven

    2014-05-01

    An external auditory stimulus induces an auditory sensation which may lead to a conscious auditory perception. Although the sensory aspect is well known, it is still a question how an auditory stimulus results in an individual's conscious percept. To unravel the uncertainties concerning the neural correlates of a conscious auditory percept, event-related potentials may serve as a useful tool. In the current review we mainly wanted to shed light on the perceptual aspects of auditory processing and therefore we mainly focused on the auditory late-latency responses. Moreover, there is increasing evidence that perception is an active process in which the brain searches for the information it expects to be present, suggesting that auditory perception requires the presence of both bottom-up, i.e. sensory and top-down, i.e. prediction-driven processing. Therefore, the auditory evoked potentials will be interpreted in the context of the Bayesian brain model, in which the brain predicts which information it expects and when this will happen. The internal representation of the auditory environment will be verified by sensation samples of the environment (P50, N100). When this incoming information violates the expectation, it will induce the emission of a prediction error signal (Mismatch Negativity), activating higher-order neural networks and inducing the update of prior internal representations of the environment (P300). Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Assadi Morteza Khalaji

    2014-07-01

    Full Text Available In this paper solar energy potential has been estimated by two methods which are clearness index and artificial network (ANN methods. The selected region is Seri Iskandar, Perak (4°24´latitude, 100°58´E longitude, 24 m altitude. Experimental data (monthly average daily radiation on horizontal surface was obtained from UTP solar research site in UTP campus. The data include the period of 2010 to 2012 and were used for testing the artificial neural network model and also for determination of clearness index. Also the experimental data of the three meteorological, Ipoh, Bayan Lepas & KLIA were used in calculating the clearness index and for training the neural network. Result shows that clearness index for Seri Iskandar is 0.52, the highest radiation is on February (20.45 MJ/m2/day, annual average is 18.25 MJ/m2/day and clearness index is more accurate than ANN when there is limited data supply. In general, Perak states show strong potential for solar energy application.

  15. Neo-sex chromosomes and adaptive potential in tortricid pests

    Czech Academy of Sciences Publication Activity Database

    Nguyen, Petr; Sýkorová, Miroslava; Šíchová, Jindra; Kůta, Václav; Dalíková, Martina; Čapková Frydrychová, Radmila; Neven, L. G.; Sahara, K.; Marec, František

    2013-01-01

    Roč. 110, č. 17 (2013), s. 6931-6936 ISSN 0027-8424 R&D Projects: GA ČR GA523/09/2106; GA AV ČR IAA600960925 Grant - others:Building up Modern Biotechnologies for Agriculture project(CZ) FP7-REGPOT-2008-1; Japan Society for the Promotion of Science Grant(JP) 23380030; GA JU(CZ) 137/2010/P; International Atomic Energy Agency, Vienna(AT) Research Agreement 15838 Institutional support: RVO:60077344 Keywords : adaptive evolution * leaf-rollers * performance genes Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 9.809, year: 2013 http://www.pnas.org/content/110/17/6931.full.pdf+html

  16. Vulnerability and adaptation to potential impacts of climate change

    International Nuclear Information System (INIS)

    Omenda, T.O.; Kariuki, J.G.; Mbuthi, P.N.

    1998-01-01

    Climate in Kenya is controlled by the seasonal southward and northward movements of the Inter-Tropical Convergence zone (ITCZ).The effects of ITCZ produces two rainy seasons namely the 'long rains' in April/May and the 'short rains' in October/November. Following the build up of greenhouse gases such as carborn dioxide and methane in the earth's atmosphere, a variety of changes is expected in climatic conditions. The study analyses the sensivity of the lower Tana Basin to climate change while specific objectives include: to determine the effects of climate change on water supply in Tana River Basin; to assess the posible effect of climate change on the ground water resourse in the basin; to make some suggestions on possible adaptation measures that may be adopted to cope with the possible impacts of climate change for the Tana Basin

  17. Spatial adaptation of the cortical visual evoked potential of the cat.

    Science.gov (United States)

    Bonds, A B

    1984-06-01

    Adaptation that is spatially specific for the adapting pattern has been seen psychophysically in humans. This is indirect evidence for independent analyzers (putatively single units) that are specific for orientation and spatial frequency in the human visual system, but it is unclear how global adaptation characteristics may be related to single unit performance. Spatially specific adaptation was sought in the cat visual evoked potential (VEP), with a view towards relating this phenomenon with what we know of cat single units. Adaptation to sine-wave gratings results in a temporary loss of cat VEP amplitude, with induction and recovery similar to that seen in human psychophysical experiments. The amplitude loss was specific for both the spatial frequency and orientation of the adapting pattern. The bandwidth of adaptation was not unlike the average selectivity of a population of cat single units.

  18. Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

    Science.gov (United States)

    Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

    2009-10-01

    A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

  19. Construction of an interatomic potential for zinc oxide surfaces by high-dimensional neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2011-07-01

    Zinc oxide (ZnO) is a technologically important material with many applications, e.g. in heterogeneous catalysis. For theoretical studies of the structural properties of ZnO surfaces, defects, and crystal structures it is necessary to simulate large systems over long time-scales with sufficient accuracy. Often, the required system size is not accessible by computationally rather demanding density-functional theory (DFT) calculations. Recently, artificial Neural Networks (NN) trained to first principles data have shown to provide accurate potential-energy surfaces (PESs) for condensed systems. We present the construction and analysis of a NN PES for ZnO. The structural and energetic properties of bulk ZnO and ZnO surfaces are investigated using this potential and compared to DFT calculations.

  20. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

    Science.gov (United States)

    Jose, K V Jovan; Artrith, Nongnuch; Behler, Jörg

    2012-05-21

    An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.

  1. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  2. The General Adaptation Syndrome: Potential misapplications to resistance exercise.

    Science.gov (United States)

    Buckner, Samuel L; Mouser, J Grant; Dankel, Scott J; Jessee, Matthew B; Mattocks, Kevin T; Loenneke, Jeremy P

    2017-11-01

    Within the resistance training literature, one of the most commonly cited tenets with respect to exercise programming is the "General Adaptation Syndrome" (GAS). The GAS is cited as a central theory behind the periodization of resistance exercise. However, after examining the original stress research by Hans Selye, the applications of GAS to resistance exercise may not be appropriate. To examine the original work of Hans Selye, as well as the original papers through which the GAS was established as a central theory for periodized resistance exercise. We conducted a review of Selye's work on the GAS, as well as the foundational papers through which this concept was applied to resistance exercise. The work of Hans Selye focused on the universal physiological stress responses noted upon exposure to toxic levels of a variety of pharmacological agents and stimuli. The extrapolations that have been made to resistance exercise appear loosely based on this concept and may not be an appropriate basis for application of the GAS to resistance exercise. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  3. Internal mechanisms underlying anticipatory language processing: Evidence from event-related-potentials and neural oscillations.

    Science.gov (United States)

    Li, Xiaoqing; Zhang, Yuping; Xia, Jinyan; Swaab, Tamara Y

    2017-07-28

    Although numerous studies have demonstrated that the language processing system can predict upcoming content during comprehension, there is still no clear picture of the anticipatory stage of predictive processing. This electroencephalograph study examined the cognitive and neural oscillatory mechanisms underlying anticipatory processing during language comprehension, and the consequences of this prediction for bottom-up processing of predicted/unpredicted content. Participants read Mandarin Chinese sentences that were either strongly or weakly constraining and that contained critical nouns that were congruent or incongruent with the sentence contexts. We examined the effects of semantic predictability on anticipatory processing prior to the onset of the critical nouns and on integration of the critical nouns. The results revealed that, at the integration stage, the strong-constraint condition (compared to the weak-constraint condition) elicited a reduced N400 and reduced theta activity (4-7Hz) for the congruent nouns, but induced beta (13-18Hz) and theta (4-7Hz) power decreases for the incongruent nouns, indicating benefits of confirmed predictions and potential costs of disconfirmed predictions. More importantly, at the anticipatory stage, the strongly constraining context elicited an enhanced sustained anterior negativity and beta power decrease (19-25Hz), which indicates that strong prediction places a higher processing load on the anticipatory stage of processing. The differences (in the ease of processing and the underlying neural oscillatory activities) between anticipatory and integration stages of lexical processing were discussed with regard to predictive processing models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Regenerative medicine using adult neural stem cells: the potential for diabetes therapy and other pharmaceutical applications

    Institute of Scientific and Technical Information of China (English)

    Tomoko Kuwabara; Makoto Asashima

    2012-01-01

    Neural stem cells (NSCs),which are responsible for continuous neurogenesis during the adult stage,are present in human adults.The typical neurogenic regions are the hippocampus and the subventricular zone; recent studies have revealed that NSCs also exist in the olfactory bulb.Olfactory bulb-derived neural stem cells (OB NSCs) have the potential to be used in therapeutic applications and can be easily harvested without harm to the patient.Through the combined influence of extrinsic cues and innate programming,adult neurogenesis is a finely regulated process occurring in a specialized cellular environment,a niche.Understanding the regulatory mechanisms of adult NSCs and their cellular niche is not only important to understand the physiological roles of neurogenesis in adulthood,but also to provide the knowledge necessary for developing new therapeutic applications using adult NSCs in other organs with similar regulatory environments.Diabetes is a devastating disease affecting more than 200 million people worldwide.Numerous diabetic patients suffer increased symptom severity after the onset,involving complications such as retinopathy and nephropathy.Therefore,the development of treatments for fundamental diabetes is important.The utilization of autologous cells from patients with diabetes may address challenges regarding the compatibility of donor tissues as well as provide the means to naturally and safely restore function,reducing future risks while also providing a long-term cure.Here,we review recent findings regarding the use of adult OB NSCs as a potential diabetes cure,and discuss the potential of OB NSC-based pharmaceutical applications for neuronal diseases and mental disorders.

  5. Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

    Science.gov (United States)

    Li, Wenwen; Ando, Yasunobu; Minamitani, Emi; Watanabe, Satoshi

    2017-12-01

    To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.

  6. Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

    Directory of Open Access Journals (Sweden)

    Mahesh Jangid

    2018-02-01

    Full Text Available Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

  7. Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

    Directory of Open Access Journals (Sweden)

    Fengxia Xu

    2014-01-01

    Full Text Available U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

  8. Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism.

    Science.gov (United States)

    Liu, Zhi; Chen, Ci; Zhang, Yun; Chen, C L P

    2015-03-01

    To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.

  9. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data

    Energy Technology Data Exchange (ETDEWEB)

    Sozen, Adnan; Ozalp, Mehmet [Gazi Univ., Mechanical Education Dept., Ankara (Turkey); Arcaklioglu, Erol [Krkkale Univ., Mechanical Engineering Dept., Krkkale (Turkey)

    2004-11-01

    Turkey is located at the Mediterranean at 36 deg and 42 deg N latitudes and has a typical Mediterranean climate. The solar energy potential is very high in Turkey. The yearly average solar radiation is 3.6 kW h/m{sup 2} day, and the total yearly radiation period is {approx}2610 h. This study consists of two cases. Firstly, the main focus of this study is to put forward the solar energy potential in Turkey using artificial neural networks (ANNs). Secondly, in this study, the best approach was investigated for each station by using different learning algorithms and a logistic sigmoid transfer function in the neural network with developed software. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (Ankara, Samsun, Edirne, Istanbul-Goztepe, Van, Izmir, Denizli, Sanl urfa, Mersin, Adana, Gaziantep, Ayd n, Bursa, Diyarbak r, Yozgat, Antalya and Mugla) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration and mean temperature) are used in the input layer of the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.735% and R{sup 2} values were found to be about 99.893% for the testing stations. However, these values were found to be 4.398% and 99.965% for the training stations. The trained and tested ANN models show greater accuracy for evaluating the solar resource possibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar potential values from the ANN are given in the form of monthly maps. These maps are of prime importance for different working disciplines, like scientists, architects, meteorologists and solar engineers, in Turkey. The predictions from the ANN models could enable scientists to locate and design solar energy systems in Turkey and determine the

  10. Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

    Science.gov (United States)

    Jia, Zi-Jun; Song, Yong-Duan

    2017-06-01

    This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

  11. Dyslexics' faster decay of implicit memory for sounds and words is manifested in their shorter neural adaptation.

    Science.gov (United States)

    Jaffe-Dax, Sagi; Frenkel, Or; Ahissar, Merav

    2017-01-24

    Dyslexia is a prevalent reading disability whose underlying mechanisms are still disputed. We studied the neural mechanisms underlying dyslexia using a simple frequency-discrimination task. Though participants were asked to compare the two tones in each trial, implicit memory of previous trials affected their responses. We hypothesized that implicit memory decays faster among dyslexics. We tested this by increasing the temporal intervals between consecutive trials, and by measuring the behavioral impact and ERP responses from the auditory cortex. Dyslexics showed a faster decay of implicit memory effects on both measures, with similar time constants. Finally, faster decay of implicit memory also characterized the impact of sound regularities in benefitting dyslexics' oral reading rate. Their benefit decreased faster as a function of the time interval from the previous reading of the same non-word. We propose that dyslexics' shorter neural adaptation paradoxically accounts for their longer reading times, since it reduces their temporal window of integration of past stimuli, resulting in noisier and less reliable predictions for both simple and complex stimuli. Less reliable predictions limit their acquisition of reading expertise.

  12. Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

    Science.gov (United States)

    Habibi, Hamed; Rahimi Nohooji, Hamed; Howard, Ian

    2017-09-01

    Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

  13. High-order tracking differentiator based adaptive neural control of a flexible air-breathing hypersonic vehicle subject to actuators constraints.

    Science.gov (United States)

    Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen

    2015-09-01

    In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Adaptive electric potential sensors for smart signal acquisition and processing

    International Nuclear Information System (INIS)

    Prance, R J; Beardsmore-Rust, S; Prance, H; Harland, C J; Stiffell, P B

    2007-01-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly

  15. Adaptive electric potential sensors for smart signal acquisition and processing

    Science.gov (United States)

    Prance, R. J.; Beardsmore-Rust, S.; Prance, H.; Harland, C. J.; Stiffell, P. B.

    2007-07-01

    Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly.

  16. Theoretical and Methodological Aspects of Assessment of the Adaptation Potential of Personnel

    Directory of Open Access Journals (Sweden)

    Sesina Iryna M.

    2014-02-01

    Full Text Available The article is devoted to development of theoretical and methodological recommendations with respect to assessment of the adaptation potential of employees as an important prerequisite of development of employees and ensuring competitiveness of an enterprise. It contains the author’s interpretation of the adaptation potential as a possibility of adjusting to the environment with the aim of achieving socio-economic goals of an enterprise. Adaptation potential is a property of a person as a performer of labour functions and ability to master new methods of work, adjustment to new labour conditions, processing of information and also a communicative property. At the same time adaptation potential is an aggregate of motivational, professional, information and integration components of a person. For assessing the adaptation potential it is proposed to combine 360 degrees method and method of paired comparison, which facilitates increase of trustworthiness of results. The author marks out some criteria of assessment of the adaptation potential: ratio of professional experience, ratio of official experience, ratio of efficiency of work, independence in mastering new methods of work, fast adjustment to new labour conditions, ability to quickly process big volumes of information, mobility, high level of productivity under different labour conditions, sharpness of wit in different production situations, ability to form interpersonal relations in a collective and psychological features.

  17. Optimization and control of a small angle ion source using an adaptive neural network controller

    Energy Technology Data Exchange (ETDEWEB)

    Brown, S.K.; Mead, W.C.; Bowling, P.S.; Jones, R.D.; Barnes, C.W.

    1993-09-01

    This project developed an automated controller based on an artificial neural network and evaluated its applicability in a real-time environment. This capability was developed within the context of a small angle negative ion source on the Discharge Test Stand at Los Alamos. The controller processes information obtained from the beam current waveform, developing a figure of merit (fom) to determine the ion source operating conditions. The fom is composed of the magnitude of the beam current, the stability of operation, and the quietness of the beam. Using no knowledge of operating conditions, the controller begins by making of rough scan of the four-dimensional operating surface. This surface uses as independent variables the anode and cathode temperatures, the hydrogen flow rate, and the arc voltage. `Me dependent variable is the fom described above. Once the rough approximation of the surface has been determined, the network formulates a model from which it determines the best operating point. The controller takes the ion source to that operating point for a reality check. As real data is fed in, the model of the operating surface is updated until the neural network`s model agrees with reality. The controller then uses a gradient ascent method to optimize the operation of the ion source. Initial tests of the controller indicate that it is remarkably capable. It has optimized the operation of the ion source on six different occasions bringing the beam to excellent quality and stability.

  18. Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks

    International Nuclear Information System (INIS)

    El-Kaliouby, Hesham M; Al-Garni, Mansour A

    2009-01-01

    The modular neural network (MNN) inversion method has been used for inversion of self-potential (SP) data anomalies caused by 2D inclined sheets of infinite horizontal extent. The analysed parameters are the depth (h), the half-width (a), the inclination (α), the zero distance from the origin (x o ) and the polarization amplitude (k). The MNN inversion has been first tested on a synthetic example and then applied to two field examples from the Surda area of Rakha mines, India, and Kalava fault zone, India. The effect of random noise has been studied, and the technique showed satisfactory results. The inversion results show good agreement with the measured field data compared with other inversion techniques in use

  19. Neural network approach in multichannel auditory event-related potential analysis.

    Science.gov (United States)

    Wu, F Y; Slater, J D; Ramsay, R E

    1994-04-01

    Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

  20. High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium

    Science.gov (United States)

    Schran, Christoph; Uhl, Felix; Behler, Jörg; Marx, Dominik

    2018-03-01

    The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol-1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.

  1. Spontaneous calcium transients in human neural progenitor cells mediated by transient receptor potential channels.

    Science.gov (United States)

    Morgan, Peter J; Hübner, Rayk; Rolfs, Arndt; Frech, Moritz J

    2013-09-15

    Calcium signals affect many developmental processes, including proliferation, migration, survival, and apoptosis, processes that are of particular importance in stem cells intended for cell replacement therapies. The mechanisms underlying Ca(2+) signals, therefore, have a role in determining how stem cells respond to their environment, and how these responses might be controlled in vitro. In this study, we examined the spontaneous Ca(2+) activity in human neural progenitor cells during proliferation and differentiation. Pharmacological characterization indicates that in proliferating cells, most activity is the result of transient receptor potential (TRP) channels that are sensitive to Gd(3+) and La(3+), with the more subtype selective antagonist Ruthenium red also reducing activity, suggesting the involvement of transient receptor potential vanilloid (TRPV) channels. In differentiating cells, Gd(3+) and La(3+)-sensitive TRP channels also appear to underlie the spontaneous activity; however, no sub-type-specific antagonists had any effect. Protein levels of TRPV2 and TRPV3 decreased in differentiated cells, which is demonstrated by western blot. Thus, it appears that TRP channels represent the main route of Ca(2+) entry in human neural progenitor cells (hNPCs), but the responsible channel types are subject to substitution under differentiating conditions. The level of spontaneous activity could be increased and decreased by lowering and raising the extracellular K(+) concentration. Proliferating cells in low K(+) slowed the cell cycle, with a disproportionate increased percentage of cells in G1 phase and a reduction in S phase. Taken together, these results suggest a link between external K(+) concentration, spontaneous Ca(2+) transients, and cell cycle distribution, which is able to influence the fate of stem and progenitor cells.

  2. Efficient second order Algorithms for Function Approximation with Neural Networks. Application to Sextic Potentials

    International Nuclear Information System (INIS)

    Gougam, L.A.; Taibi, H.; Chikhi, A.; Mekideche-Chafa, F.

    2009-01-01

    The problem of determining the analytical description for a set of data arises in numerous sciences and applications and can be referred to as data modeling or system identification. Neural networks are a convenient means of representation because they are known to be universal approximates that can learn data. The desired task is usually obtained by a learning procedure which consists in adjusting the s ynaptic weights . For this purpose, many learning algorithms have been proposed to update these weights. The convergence for these learning algorithms is a crucial criterion for neural networks to be useful in different applications. The aim of the present contribution is to use a training algorithm for feed forward wavelet networks used for function approximation. The training is based on the minimization of the least-square cost function. The minimization is performed by iterative second order gradient-based methods. We make use of the Levenberg-Marquardt algorithm to train the architecture of the chosen network and, then, the training procedure starts with a simple gradient method which is followed by a BFGS (Broyden, Fletcher, Glodfarb et Shanno) algorithm. The performances of the two algorithms are then compared. Our method is then applied to determine the energy of the ground state associated to a sextic potential. In fact, the Schrodinger equation does not always admit an exact solution and one has, generally, to solve it numerically. To this end, the sextic potential is, firstly, approximated with the above outlined wavelet network and, secondly, implemented into a numerical scheme. Our results are in good agreement with the ones found in the literature.

  3. A Cognitive and Neural Model for Adaptive Emotion Reading by Mirroring Preparation States and Hebbian Learning

    NARCIS (Netherlands)

    Bosse, T.; Memon, Z.A.; Treur, J.

    2012-01-01

    Two types of modelling approaches exist to reading an observed person's emotions: with or without making use of the observing person's own emotions. This paper focuses on an integrated approach that combines both types of approaches in an adaptive manner. The proposed models were inspired by recent

  4. Neural network-based optimal adaptive output feedback control of a helicopter UAV.

    Science.gov (United States)

    Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

    2013-07-01

    Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

  5. The changing brain--insights into the mechanisms of neural and behavioral adaptation to the environment

    DEFF Research Database (Denmark)

    Bergersen, L H; Bramham, C R; Hugdahl, K

    2013-01-01

    level and behavior. Thus a single amino acid change in a transcriptional repressor can disrupt gene regulation through neural activity (Greenberg). Deep sequencing analysis of the neuropil transcriptome indicates that a large fraction of the synaptic proteome is synthesized in situ in axons...... and dendrites, permitting local regulation (Schuman). The nature of the 'reset' function that makes animals dependent of sleep is being revealed (Cirelli). Maternal behavior can cause changes in gene expression that stably modify behavior in the offspring (Meaney). Removal of a single sensory channel protein...... in the vomero-nasal organ can switch off male-specific and switch on female-specific innate behavior of mice in response to environmental stimulation (Dulac). Innate behaviors can be stably transmitted from parent to offspring through generations even when those behaviors cannot be expressed, as illustrated...

  6. The influence of cochlear traveling wave and neural adaptation on auditory brainstem responses

    DEFF Research Database (Denmark)

    Junius, D.; Dau, Torsten

    2005-01-01

    of the responses to the single components, as a function of stimulus level. In the first experiment, a single rising chirp was temporally and spectrally embedded in two steady-state tones. In the second experiment, the stimulus consisted of a continuous alternating train of chirps: each rising chirp was followed...... by the temporally reversed (falling) chirp. In both experiments, the transitions between stimulus components were continuous. For stimulation levels up to approximately 70 dB SPL, the responses to the embedded chirp corresponded to the responses to the single chirp. At high stimulus levels (80-100 dB SPL......), disparities occurred between the responses, reflecting a nonlinearity in the processing when neural activity is integrated across frequency. In the third experiment, the effect of within-train rate on wave-V response was investigated. The response to the chirp presented at a within-train rate of 95 Hz...

  7. PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    A. Cancelier

    Full Text Available Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

  8. The neural response properties and cortical organization of a rapidly adapting muscle sensory group response that overlaps with the frequencies that elicit the kinesthetic illusion.

    Science.gov (United States)

    Marasco, Paul D; Bourbeau, Dennis J; Shell, Courtney E; Granja-Vazquez, Rafael; Ina, Jason G

    2017-01-01

    Kinesthesia is the sense of limb movement. It is fundamental to efficient motor control, yet its neurophysiological components remain poorly understood. The contributions of primary muscle spindles and cutaneous afferents to the kinesthetic sense have been well studied; however, potential contributions from muscle sensory group responses that are different than the muscle spindles have not been ruled out. Electrophysiological recordings in peripheral nerves and brains of male Sprague Dawley rats with a degloved forelimb preparation provide evidence of a rapidly adapting muscle sensory group response that overlaps with vibratory inputs known to generate illusionary perceptions of limb movement in humans (kinesthetic illusion). This group was characteristically distinct from type Ia muscle spindle fibers, the receptor historically attributed to limb movement sensation, suggesting that type Ia muscle spindle fibers may not be the sole carrier of kinesthetic information. The sensory-neural structure of muscles is complex and there are a number of possible sources for this response group; with Golgi tendon organs being the most likely candidate. The rapidly adapting muscle sensory group response projected to proprioceptive brain regions, the rodent homolog of cortical area 3a and the second somatosensory area (S2), with similar adaption and frequency response profiles between the brain and peripheral nerves. Their representational organization was muscle-specific (myocentric) and magnified for proximal and multi-articulate limb joints. Projection to proprioceptive brain areas, myocentric representational magnification of muscles prone to movement error, overlap with illusionary vibrational input, and resonant frequencies of volitional motor unit contraction suggest that this group response may be involved with limb movement processing.

  9. The neural response properties and cortical organization of a rapidly adapting muscle sensory group response that overlaps with the frequencies that elicit the kinesthetic illusion.

    Directory of Open Access Journals (Sweden)

    Paul D Marasco

    Full Text Available Kinesthesia is the sense of limb movement. It is fundamental to efficient motor control, yet its neurophysiological components remain poorly understood. The contributions of primary muscle spindles and cutaneous afferents to the kinesthetic sense have been well studied; however, potential contributions from muscle sensory group responses that are different than the muscle spindles have not been ruled out. Electrophysiological recordings in peripheral nerves and brains of male Sprague Dawley rats with a degloved forelimb preparation provide evidence of a rapidly adapting muscle sensory group response that overlaps with vibratory inputs known to generate illusionary perceptions of limb movement in humans (kinesthetic illusion. This group was characteristically distinct from type Ia muscle spindle fibers, the receptor historically attributed to limb movement sensation, suggesting that type Ia muscle spindle fibers may not be the sole carrier of kinesthetic information. The sensory-neural structure of muscles is complex and there are a number of possible sources for this response group; with Golgi tendon organs being the most likely candidate. The rapidly adapting muscle sensory group response projected to proprioceptive brain regions, the rodent homolog of cortical area 3a and the second somatosensory area (S2, with similar adaption and frequency response profiles between the brain and peripheral nerves. Their representational organization was muscle-specific (myocentric and magnified for proximal and multi-articulate limb joints. Projection to proprioceptive brain areas, myocentric representational magnification of muscles prone to movement error, overlap with illusionary vibrational input, and resonant frequencies of volitional motor unit contraction suggest that this group response may be involved with limb movement processing.

  10. Behavioral and neural Darwinism: selectionist function and mechanism in adaptive behavior dynamics.

    Science.gov (United States)

    McDowell, J J

    2010-05-01

    An evolutionary theory of behavior dynamics and a theory of neuronal group selection share a common selectionist framework. The theory of behavior dynamics instantiates abstractly the idea that behavior is selected by its consequences. It implements Darwinian principles of selection, reproduction, and mutation to generate adaptive behavior in virtual organisms. The behavior generated by the theory has been shown to be quantitatively indistinguishable from that of live organisms. The theory of neuronal group selection suggests a mechanism whereby the abstract principles of the evolutionary theory may be implemented in the nervous systems of biological organisms. According to this theory, groups of neurons subserving behavior may be selected by synaptic modifications that occur when the consequences of behavior activate value systems in the brain. Together, these theories constitute a framework for a comprehensive account of adaptive behavior that extends from brain function to the behavior of whole organisms in quantitative detail. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  11. Disentangling the Attention Network Test: Behavioral, Event Related Potentials and neural source analyses.

    Directory of Open Access Journals (Sweden)

    Alejandro eGalvao-Carmona

    2014-10-01

    Full Text Available Background. The study of the attentional system remains a challenge for current neuroscience. The Attention Network Test (ANT was designed to study simultaneously three different attentional networks (alerting, orienting and executive based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event Related Potentials (ERPs and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioural measures. Results. This study shows that there is a basic level of alerting (tonic alerting in the no cue condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the no cue condition; a late modulation triggered by the central cue condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions. The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human

  12. Adaptive control strategy for ECRH negative high-voltage power supply based on CMAC neural network

    International Nuclear Information System (INIS)

    Luo Xiaoping; Du Pengying; Du Shaowu

    2011-01-01

    In order to solve the problem that the negative high-voltage power supply in an electron cyclotron resonance heating (ECRH) system can not satisfy the requirements because of the nonlinearity and sensitivity, the direct inverse model control strategy was proposed by using cerebellar model articulation controller(CMAC) for better control, and experiments were carried out to study the system performances with CMAC tracing dynamic signals. The results show that this strategy is strong in self-learning and self-adaptation and easy to be realized. (authors)

  13. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

    Science.gov (United States)

    Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

    2017-05-30

    The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

  14. The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

    Directory of Open Access Journals (Sweden)

    Gaining Han

    2017-05-01

    Full Text Available The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS, the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

  15. Using Adaptive Neural-Fuzzy Inference Systems (ANFIS for Demand Forecasting and an Application

    Directory of Open Access Journals (Sweden)

    Onur Doğan

    2016-06-01

    Full Text Available Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

  16. Neural 17β-estradiol facilitates long-term potentiation in the hippocampal CA1 region.

    Science.gov (United States)

    Grassi, S; Tozzi, A; Costa, C; Tantucci, M; Colcelli, E; Scarduzio, M; Calabresi, P; Pettorossi, V E

    2011-09-29

    In the hippocampal formation many neuromodulators are possibly implied in the synaptic plasticity such as the long-term potentiation (LTP) induced by high-frequency stimulation (HFS) of afferent fibers. We investigated the involvement of locally synthesized neural 17β-estradiol (nE(2)) in the induction of HFS-LTP in hippocampal slices from male rats by stimulating the Schaffer collateral fibers and recording the evoked field excitatory postsynaptic potential (fEPSP) in the CA1 region. We demonstrated that either the blockade of nE(2) synthesis by the aromatase inhibitor letrozole, or the antagonism of E(2) receptors (ERs) by ICI 182,780 did not prevent the induction of HFS-LTP, but reduced its amplitude by ∼60%, without influencing its maintenance. Moreover, letrozole and ICI 182,780 did not affect the first short-term post-tetanic component of LTP and the paired-pulse facilitation (PPF). These findings demonstrate that nE(2) plays an important role in the induction phase of HFS-dependent LTP. Since the basal responses were not affected by the blocking agents, we suggest that the synthesis of nE(2) is induced or enhanced by HFS through aromatase activation. In this context, the local production of nE(2) seems to be a very effective mechanism to modulate the amplitude of LTP. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

  17. Potential Habitat Modelling of Ferula ovina Using Artificial Neural Network in Fereydunshahr Region, Isfahan Province

    Directory of Open Access Journals (Sweden)

    Z. Rahmati

    2015-06-01

    Full Text Available Species distribution maps have been widely developed based on ecological niche theory together with statistical and geographical information system in plant ecology. The current study aimed to evaluate Artificial Neural Network (ANN in mapping potential habitat of Ferula ovina Boiss in Ferydunshar rangelands, Isfahan. This is known as valuable forage and medicinal species. Environmental data (independent variables and species occurrence data (dependent variable were required to determine potential habitat of a given species. Some physical and chemical soil properties, climate and physiographic variables were mapped for the entire studied area using krigging and inverse distance weighting methods. F. ovina occurrence data were collected from 278 sites including 137 presence and 141 absence sites. The relationships between the studied environmental variables and F. ovina occurrence data were explored using ANN method. According to the sensitivity analysis, occurrence of F. ovina mostly correlated with silt and sand percentage, elevation slope, and organic matter. Model evaluation based on Kappa coefficient (0.66 and Receiver operating characteristic (ROC=0.9 showed good model fitness in relation to reality on local scales. The ANN technique enables managers to identify appropriate areas for rehabilitation practices such as direct seeding and planting.                       

  18. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.

    Science.gov (United States)

    Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

    2017-05-28

    We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

  19. Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study

    International Nuclear Information System (INIS)

    Benedetti, Miriam; Cesarotti, Vittorio; Introna, Vito; Serranti, Jacopo

    2016-01-01

    Highlights: • A methodology to enable energy consumption control automation is proposed. • The methodology is based on the use of Artificial Neural Networks. • A method to control the accuracy of the model over time is proposed. • Two methods to enable automatic retraining of the network are proposed. • Retraining methods are evaluated on their accuracy over time. - Abstract: Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses’ output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge. This paper presents a whole new approach to energy consumption control, proposing a methodology based on Artificial Neural Networks (ANNs) and aimed at creating an automatic energy consumption control system. First of all, three different structures of neural networks are proposed and trained using a huge amount of data. Three different performance indicators are then used to identify the most suitable structure, which is implemented to create an energy consumption control tool. In addition, considering that

  20. Integrated low noise low power interface for neural bio-potentials recording and conditioning

    Science.gov (United States)

    Bottino, Emanuele; Martinoia, Sergio; Valle, Maurizio

    2005-06-01

    The recent progress in both neurobiology and microelectronics suggests the creation of new, powerful tools to investigate the basic mechanisms of brain functionality. In particular, a lot of efforts are spent by scientific community to define new frameworks devoted to the analysis of in-vitro cultured neurons. One possible approach is recording their spiking activity to monitor the coordinated cellular behaviour and get insights about neural plasticity. Due to the nature of neurons action-potentials, when considering the design of an integrated microelectronic-based recording system, a number of problems arise. First, one would desire to have a high number of recording sites (i.e. several hundreds): this poses constraints on silicon area and power consumption. In this regard, our aim is to integrate-through on-chip post-processing techniques-hundreds of bio-compatible microsensors together with CMOS standard-process low-power (i.e. some tenths of uW per channel) conditioning electronics. Each recording channel is provided with sampling electronics to insure synchronous recording so that, for example, cross-correlation between signals coming from different sites can be performed. Extra-cellular potentials are in the range of [50-150] uV, so a comparison in terms of noise-efficiency was carried out among different architectures and very low-noise pre-amplification electronics (i.e. less than 5 uVrms) was designed. As spikes measurements are made with respect to the voltage of a reference electrode, we opted for an AC-coupled differential-input preamplifier provided with band-pass filtering capability. To achieve this, we implemented large time-constant (up to seconds) integrated components in the preamp feedback path. Thus, we got rid also of random slow-drifting DC-offsets and common mode signals. The paper will present our achievements in the design and implementation of a fully integrated bio-abio interface to record neural spiking activity. In particular

  1. The predator and prey behaviors of crabs: from ecology to neural adaptations.

    Science.gov (United States)

    Tomsic, Daniel; Sztarker, Julieta; Berón de Astrada, Martín; Oliva, Damián; Lanza, Estela

    2017-07-01

    Predator avoidance and prey capture are among the most vital of animal behaviors. They require fast reactions controlled by comparatively straightforward neural circuits often containing giant neurons, which facilitates their study with electrophysiological techniques. Naturally occurring avoidance behaviors, in particular, can be easily and reliably evoked in the laboratory, enabling their neurophysiological investigation. Studies in the laboratory alone, however, can lead to a biased interpretation of an animal's behavior in its natural environment. In this Review, we describe current knowledge - acquired through both laboratory and field studies - on the visually guided escape behavior of the crab Neohelice granulata Analyses of the behavioral responses to visual stimuli in the laboratory have revealed the main characteristics of the crab's performance, such as the continuous regulation of the speed and direction of the escape run, or the enduring changes in the strength of escape induced by learning and memory. This work, in combination with neuroanatomical and electrophysiological studies, has allowed the identification of various giant neurons, the activity of which reflects most essential aspects of the crabs' avoidance performance. In addition, behavioral analyses performed in the natural environment reveal a more complex picture: crabs make use of much more information than is usually available in laboratory studies. Moreover, field studies have led to the discovery of a robust visually guided chasing behavior in Neohelice Here, we describe similarities and differences in the results obtained between the field and the laboratory, discuss the sources of any differences and highlight the importance of combining the two approaches. © 2017. Published by The Company of Biologists Ltd.

  2. Adaptive genetic potential of coniferous forest tree species under climate change: implications for sustainable forest management

    Science.gov (United States)

    Mihai, Georgeta; Birsan, Marius-Victor; Teodosiu, Maria; Dumitrescu, Alexandru; Daia, Mihai; Mirancea, Ionel; Ivanov, Paula; Alin, Alexandru

    2017-04-01

    Mountain ecosystems are extremely vulnerable to climate change. The real potential for adaptation depends upon the existence of a wide genetic diversity in trees populations, upon the adaptive genetic variation, respectively. Genetic diversity offers the guarantee that forest species can survive, adapt and evolve under the influence of changing environmental conditions. The aim of this study is to evaluate the genetic diversity and adaptive genetic potential of two local species - Norway spruce and European silver fir - in the context of regional climate change. Based on data from a long-term provenance experiments network and climate variables spanning over more than 50 years, we have investigated the impact of climatic factors on growth performance and adaptation of tree species. Our results indicate that climatic and geographic factors significantly affect forest site productivity. Mean annual temperature and annual precipitation amount were found to be statistically significant explanatory variables. Combining the additive genetic model with the analysis of nuclear markers we obtained different images of the genetic structure of tree populations. As genetic indicators we used: gene frequencies, genetic diversity, genetic differentiation, genetic variance, plasticity. Spatial genetic analyses have allowed identifying the genetic centers holding high genetic diversity which will be valuable sources of gene able to buffer the negative effects of future climate change. Correlations between the marginal populations and in the optimal vegetation, between the level of genetic diversity and ecosystem stability, will allow the assessment of future risks arising from current genetic structure. Therefore, the strategies for sustainable forest management have to rely on the adaptive genetic variation and local adaptation of the valuable genetic resources. This work was realized within the framework of the project GENCLIM (Evaluating the adaptive potential of the main

  3. Spike-threshold adaptation predicted by membrane potential dynamics in vivo.

    Directory of Open Access Journals (Sweden)

    Bertrand Fontaine

    2014-04-01

    Full Text Available Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

  4. Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Zhang, Hongwei; Sun, Gang

    2014-08-01

    This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

  5. An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: Application for isolated sites in Algeria

    International Nuclear Information System (INIS)

    Mellit, A.; Benghanem, M.; Hadj Arab, A.; Guessoum, G.

    2004-07-01

    In this paper we investigate, by using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. This model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) system, we need to determine the optimal sizing coefficients (K PV , K B . These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available and which are considered the most important parameters for sizing a PV system. Obtained results by classical models (analytical, numerical, analytical- numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR have been compared with experimental sizing coefficients in order to illustrate the accuracy of the results of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and even handle a number of unusual cases, the unknown validation sizing coefficients set produced very set accurate estimation and a correlation coefficient of 98% was obtained between the calculated and that estimated by the RBF-IIR model. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria, but the methodology can be generalized using different locations over the world. (author)

  6. Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network

    International Nuclear Information System (INIS)

    Reichenbach, Tobias; Hudspeth, A J

    2012-01-01

    Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus—they exhibit phase locking—and thus provide temporal information about the tone's frequency. Humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we employ statistical and numerical methods to demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans. (paper)

  7. Hypoxia Epigenetically Confers Astrocytic Differentiation Potential on Human Pluripotent Cell-Derived Neural Precursor Cells

    Directory of Open Access Journals (Sweden)

    Tetsuro Yasui

    2017-06-01

    Full Text Available Human neural precursor cells (hNPCs derived from pluripotent stem cells display a high propensity for neuronal differentiation, but they require long-term culturing to differentiate efficiently into astrocytes. The mechanisms underlying this biased fate specification of hNPCs remain elusive. Here, we show that hypoxia confers astrocytic differentiation potential on hNPCs through epigenetic gene regulation, and that this was achieved by cooperation between hypoxia-inducible factor 1α and Notch signaling, accompanied by a reduction of DNA methylation level in the promoter region of a typical astrocyte-specific gene, Glial fibrillary acidic protein. Furthermore, we found that this hypoxic culture condition could be applied to rapid generation of astrocytes from Rett syndrome patient-derived hNPCs, and that these astrocytes impaired neuronal development. Thus, our findings shed further light on the molecular mechanisms regulating hNPC differentiation and provide attractive tools for the development of therapeutic strategies for treating astrocyte-mediated neurological disorders.

  8. Prediction of Potential Hit Song and Musical Genre Using Artificial Neural Networks

    Science.gov (United States)

    Monterola, Christopher; Abundo, Cheryl; Tugaff, Jeric; Venturina, Lorcel Ericka

    Accurately quantifying the goodness of music based on the seemingly subjective taste of the public is a multi-million industry. Recording companies can make sound decisions on which songs or artists to prioritize if accurate forecasting is achieved. We extract 56 single-valued musical features (e.g. pitch and tempo) from 380 Original Pilipino Music (OPM) songs (190 are hit songs) released from 2004 to 2006. Based on an effect size criterion which measures a variable's discriminating power, the 20 highest ranked features are fed to a classifier tasked to predict hit songs. We show that regardless of musical genre, a trained feed-forward neural network (NN) can predict potential hit songs with an average accuracy of ΦNN = 81%. The accuracy is about +20% higher than those of standard classifiers such as linear discriminant analysis (LDA, ΦLDA = 61%) and classification and regression trees (CART, ΦCART = 57%). Both LDA and CART are above the proportional chance criterion (PCC, ΦPCC = 50%) but are slightly below the suggested acceptable classifier requirement of 1.25*ΦPCC = 63%. Utilizing a similar procedure, we demonstrate that different genres (ballad, alternative rock or rock) of OPM songs can be automatically classified with near perfect accuracy using LDA or NN but only around 77% using CART.

  9. Discrepancy of neural response between exogenous and endogenous task switching: an event-related potentials study.

    Science.gov (United States)

    Miyajima, Maki; Toyomaki, Atsuhito; Hashimoto, Naoki; Kusumi, Ichiro; Murohashi, Harumitsu; Koyama, Tsukasa

    2012-08-01

    Task switching is a well-known cognitive paradigm to explore task-set reconfiguration processes such as rule shifting. In particular, endogenous task switching is thought to differ qualitatively from stimulus-triggered exogenous task switching. However, no previous study has examined the neural substrate of endogenous task switching. The purpose of the present study is to explore the differences between event-related potential responses to exogenous and endogenous rule switching at cue stimulus. We modified two patterns of cued switching tasks: exogenous (bottom-up) rule switching and endogenous (top-down) rule switching. In each task cue stimulus was configured to induce switching or maintaining rule. In exogenous switching tasks, late positive deflection was larger in the switch rule condition than in the maintain rule condition. However, in endogenous switching tasks late positive deflection was unexpectedly larger in the maintain-rule condition than in the switch-rule condition. These results indicate that exogenous rule switching is explicit stimulus-driven processes, whereas endogenous rule switching is implicitly parallel processes independent of external stimulus.

  10. Fibronectin promotes differentiation of neural crest progenitors endowed with smooth muscle cell potential

    International Nuclear Information System (INIS)

    Costa-Silva, Bruno; Coelho da Costa, Meline; Melo, Fernanda Rosene; Neves, Cynara Mendes; Alvarez-Silva, Marcio; Calloni, Giordano Wosgrau; Trentin, Andrea Goncalves

    2009-01-01

    The neural crest (NC) is a model system used to investigate multipotency during vertebrate development. Environmental factors control NC cell fate decisions. Despite the well-known influence of extracellular matrix molecules in NC cell migration, the issue of whether they also influence NC cell differentiation has not been addressed at the single cell level. By analyzing mass and clonal cultures of mouse cephalic and quail trunk NC cells, we show for the first time that fibronectin (FN) promotes differentiation into the smooth muscle cell phenotype without affecting differentiation into glia, neurons, and melanocytes. Time course analysis indicated that the FN-induced effect was not related to massive cell death or proliferation of smooth muscle cells. Finally, by comparing clonal cultures of quail trunk NC cells grown on FN and collagen type IV (CLIV), we found that FN strongly increased both NC cell survival and the proportion of unipotent and oligopotent NC progenitors endowed with smooth muscle potential. In contrast, melanocytic progenitors were prominent in clonogenic NC cells grown on CLIV. Taken together, these results show that FN promotes NC cell differentiation along the smooth muscle lineage, and therefore plays an important role in fate decisions of NC progenitor cells

  11. Spike frequency adaptation is a possible mechanism for control of attractor preference in auto-associative neural networks

    Science.gov (United States)

    Roach, James; Sander, Leonard; Zochowski, Michal

    Auto-associative memory is the ability to retrieve a pattern from a small fraction of the pattern and is an important function of neural networks. Within this context, memories that are stored within the synaptic strengths of networks act as dynamical attractors for network firing patterns. In networks with many encoded memories, some attractors will be stronger than others. This presents the problem of how networks switch between attractors depending on the situation. We suggest that regulation of neuronal spike-frequency adaptation (SFA) provides a universal mechanism for network-wide attractor selectivity. Here we demonstrate in a Hopfield type attractor network that neurons minimal SFA will reliably activate in the pattern corresponding to a local attractor and that a moderate increase in SFA leads to the network to converge to the strongest attractor state. Furthermore, we show that on long time scales SFA allows for temporal sequences of activation to emerge. Finally, using a model of cholinergic modulation within the cortex we argue that dynamic regulation of attractor preference by SFA could be critical for the role of acetylcholine in attention or for arousal states in general. This work was supported by: NSF Graduate Research Fellowship Program under Grant No. DGE 1256260 (JPR), NSF CMMI 1029388 (MRZ) and NSF PoLS 1058034 (MRZ & LMS).

  12. A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.

    Science.gov (United States)

    Liu, Yan-Jun; Gao, Ying; Tong, Shaocheng; Chen, C L Philip

    2016-01-01

    In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a m -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example.

  13. Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

    Directory of Open Access Journals (Sweden)

    Ja’fari A.

    2014-01-01

    Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

  14. Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay.

    Science.gov (United States)

    Hwang, Chih-Lyang; Jan, Chau

    2016-02-01

    At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.

  15. Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system

    Energy Technology Data Exchange (ETDEWEB)

    Metin Ertunc, H. [Department of Mechatronics Engineering, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey); Hosoz, Murat [Department of Mechanical Education, Kocaeli University, Umuttepe, 41380 Kocaeli (Turkey)

    2008-12-15

    This study deals with predicting the performance of an evaporative condenser using both artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. For this aim, an experimental evaporative condenser consisting of a copper tube condensing coil along with air and water circuit elements was developed and equipped with instruments used for temperature, pressure and flow rate measurements. After the condenser was connected to an R134a vapour-compression refrigeration circuit, it was operated at steady state conditions, while varying both dry and wet bulb temperatures of the air stream entering the condenser, air and water flow rates as well as pressure, temperature and flow rate of the entering refrigerant. Using some of the experimental data for training, ANN and ANFIS models for the evaporative condenser were developed. These models were used for predicting the condenser heat rejection rate, refrigerant temperature leaving the condenser along with dry and wet bulb temperatures of the leaving air stream. Although it was observed that both ANN and ANFIS models yielded a good statistical prediction performance in terms of correlation coefficient, mean relative error, root mean square error and absolute fraction of variance, the accuracies of ANFIS predictions were usually slightly better than those of ANN predictions. This study reveals that, having an extended prediction capability compared to ANN, the ANFIS technique can also be used for predicting the performance of evaporative condensers. (author)

  16. Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

    Directory of Open Access Journals (Sweden)

    Zhi-Ren Tsai

    2013-01-01

    Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

  17. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    Science.gov (United States)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  18. Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

    Science.gov (United States)

    Salehi, Mohammad Reza; Noori, Leila; Abiri, Ebrahim

    2016-11-01

    In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm2), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (-40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.

  19. Adaptive metric learning with deep neural networks for video-based facial expression recognition

    Science.gov (United States)

    Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

    2018-01-01

    Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

  20. Potential for adaptation to climate change in a coral reef fish.

    Science.gov (United States)

    Munday, Philip L; Donelson, Jennifer M; Domingos, Jose A

    2017-01-01

    Predicting the impacts of climate change requires knowledge of the potential to adapt to rising temperatures, which is unknown for most species. Adaptive potential may be especially important in tropical species that have narrow thermal ranges and live close to their thermal optimum. We used the animal model to estimate heritability, genotype by environment interactions and nongenetic maternal components of phenotypic variation in fitness-related traits in the coral reef damselfish, Acanthochromis polyacanthus. Offspring of wild-caught breeding pairs were reared for two generations at current-day and two elevated temperature treatments (+1.5 and +3.0 °C) consistent with climate change projections. Length, weight, body condition and metabolic traits (resting and maximum metabolic rate and net aerobic scope) were measured at four stages of juvenile development. Additive genetic variation was low for length and weight at 0 and 15 days posthatching (dph), but increased significantly at 30 dph. By contrast, nongenetic maternal effects on length, weight and body condition were high at 0 and 15 dph and became weaker at 30 dph. Metabolic traits, including net aerobic scope, exhibited high heritability at 90 dph. Furthermore, significant genotype x environment interactions indicated potential for adaptation of maximum metabolic rate and net aerobic scope at higher temperatures. Net aerobic scope was negatively correlated with weight, indicating that any adaptation of metabolic traits at higher temperatures could be accompanied by a reduction in body size. Finally, estimated breeding values for metabolic traits in F2 offspring were significantly affected by the parental rearing environment. Breeding values at higher temperatures were highest for transgenerationally acclimated fish, suggesting a possible role for epigenetic mechanisms in adaptive responses of metabolic traits. These results indicate a high potential for adaptation of aerobic scope to higher temperatures

  1. Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

    Science.gov (United States)

    Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

    2017-06-23

    Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

  2. Exponential Antisynchronization Control of Stochastic Memristive Neural Networks with Mixed Time-Varying Delays Based on Novel Delay-Dependent or Delay-Independent Adaptive Controller

    Directory of Open Access Journals (Sweden)

    Minghui Yu

    2017-01-01

    Full Text Available The global exponential antisynchronization in mean square of memristive neural networks with stochastic perturbation and mixed time-varying delays is studied in this paper. Then, two kinds of novel delay-dependent and delay-independent adaptive controllers are designed. With the ability of adapting to environment changes, the proposed controllers can modify their behaviors to achieve the best performance. In particular, on the basis of the differential inclusions theory, inequality theory, and stochastic analysis techniques, several sufficient conditions are obtained to guarantee the exponential antisynchronization between the drive system and response system. Furthermore, two numerical simulation examples are provided to the validity of the derived criteria.

  3. Adapting to Changing Memory Retrieval Demands: Evidence from Event-Related Potentials

    Science.gov (United States)

    Benoit, Roland G.; Werkle-Bergner, Markus; Mecklinger, Axel; Kray, Jutta

    2009-01-01

    This study investigated preparatory processes involved in adapting to changing episodic memory retrieval demands. Event-related potentials (ERPs) were recorded while participants performed a general old/new recognition task and a specific task that also required retrieval of perceptual details. The relevant task remained either constant or changed…

  4. Joint optimization of phase diversity and adaptive optics : Demonstration of potential

    NARCIS (Netherlands)

    Korkiakoski, V.; Keller, C.U.; Doelman, N.; Fraanje, P.R.; Verhaegen, M.H.G.

    2011-01-01

    We study different possibilities to use adaptive optics (AO) and phase diversity (PD) together in a jointly optimized system. The potential of the joint system is demonstrated through numerical simulations. We find that the most significant benefits are obtained from the improved deconvolution of

  5. Chitosan scaffolds induce human dental pulp stem cells to neural differentiation: potential roles for spinal cord injury therapy.

    Science.gov (United States)

    Zhang, Jinlong; Lu, Xiaohui; Feng, Guijuan; Gu, Zhifeng; Sun, Yuyu; Bao, Guofeng; Xu, Guanhua; Lu, Yuanzhou; Chen, Jiajia; Xu, Lingfeng; Feng, Xingmei; Cui, Zhiming

    2016-10-01

    Cell-based transplantation strategies hold great potential for spinal cord injury (SCI) repair. Chitosan scaffolds have therapeutic benefits for spinal cord regeneration. Human dental pulp stem cells (DPSCs) are abundant available stem cells with low immunological incompatibility and can be considered for cell replacement therapy. The purpose of this study is to investigate the role of chitosan scaffolds in the neural differentiation of DPSCs in vitro and to assess the supportive effects of chitosan scaffolds in an animal model of SCI. DPSCs were incubated with chitosan scaffolds. Cell viability and the secretion of neurotrophic factors were analyzed. DPSCs incubated with chitosan scaffolds were treated with neural differentiation medium for 14 days and then neural genes and protein markers were analyzed by Western blot and reverse transcription plus the polymerase chain reaction. Our study revealed a higher cell viability and neural differentiation in the DPSC/chitosan-scaffold group. Compared with the control group, the levels of BDNF, GDNF, b-NGF, and NT-3 were significantly increased in the DPSC/chitosan-scaffold group. The Wnt/β-catenin signaling pathway played a key role in the neural differentiation of DPSCs combined with chitosan scaffolds. Transplantation of DPSCs together with chitosan scaffolds into an SCI rat model resulted in the marked recovery of hind limb locomotor functions. Thus, chitosan scaffolds were non-cytotoxic and provided a conducive and favorable microenvironment for the survival and neural differentiation of DPSCs. Transplantation of DPSCs might therefore be a suitable candidate for treating SCI and other neuronal degenerative diseases.

  6. Adaptation.

    Science.gov (United States)

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  7. Unstructured Grid Adaptation: Status, Potential Impacts, and Recommended Investments Toward CFD Vision 2030

    Science.gov (United States)

    Park, Michael A.; Krakos, Joshua A.; Michal, Todd; Loseille, Adrien; Alonso, Juan J.

    2016-01-01

    Unstructured grid adaptation is a powerful tool to control discretization error for Computational Fluid Dynamics (CFD). It has enabled key increases in the accuracy, automation, and capacity of some fluid simulation applications. Slotnick et al. provides a number of case studies in the CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences to illustrate the current state of CFD capability and capacity. The authors forecast the potential impact of emerging High Performance Computing (HPC) environments forecast in the year 2030 and identify that mesh generation and adaptivity continue to be significant bottlenecks in the CFD work flow. These bottlenecks may persist because very little government investment has been targeted in these areas. To motivate investment, the impacts of improved grid adaptation technologies are identified. The CFD Vision 2030 Study roadmap and anticipated capabilities in complementary disciplines are quoted to provide context for the progress made in grid adaptation in the past fifteen years, current status, and a forecast for the next fifteen years with recommended investments. These investments are specific to mesh adaptation and impact other aspects of the CFD process. Finally, a strategy is identified to diffuse grid adaptation technology into production CFD work flows.

  8. Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.

    Science.gov (United States)

    Xia, Yonghui; Yang, Zijiang; Han, Maoan

    2009-07-01

    This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.

  9. Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

    Science.gov (United States)

    Li, Zhijun; Su, Chun-Yi

    2013-09-01

    In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

  10. Adaptation

    International Development Research Centre (IDRC) Digital Library (Canada)

    building skills, knowledge or networks on adaptation, ... the African partners leading the AfricaAdapt network, together with the UK-based Institute of Development Studies; and ... UNCCD Secretariat, Regional Coordination Unit for Africa, Tunis, Tunisia .... 26 Rural–urban Cooperation on Water Management in the Context of.

  11. Differentiation of Equine Mesenchymal Stromal Cells into Cells of Neural Lineage: Potential for Clinical Applications

    Directory of Open Access Journals (Sweden)

    Claudia Cruz Villagrán

    2014-01-01

    Full Text Available Mesenchymal stromal cells (MSCs are able to differentiate into extramesodermal lineages, including neurons. Positive outcomes were obtained after transplantation of neurally induced MSCs in laboratory animals after nerve injury, but this is unknown in horses. Our objectives were to test the ability of equine MSCs to differentiate into cells of neural lineage in vitro, to assess differences in morphology and lineage-specific protein expression, and to investigate if horse age and cell passage number affected the ability to achieve differentiation. Bone marrow-derived MSCs were obtained from young and adult horses. Following demonstration of stemness, MSCs were neurally induced and microscopically assessed at different time points. Results showed that commercially available nitrogen-coated tissue culture plates supported proliferation and differentiation. Morphological changes were immediate and all the cells displayed a neural crest-like cell phenotype. Expression of neural progenitor proteins, was assessed via western blot or immunofluorescence. In our study, MSCs generated from young and middle-aged horses did not show differences in their ability to undergo differentiation. The effect of cell passage number, however, is inconsistent and further experiments are needed. Ongoing work is aimed at transdifferentiating these cells into Schwann cells for transplantation into a peripheral nerve injury model in horses.

  12. Implementation of a model reference adaptive control system using neural network to control a fast breeder reactor evaporator

    International Nuclear Information System (INIS)

    Ugolini, D.; Yoshikawa, S.; Endou, A.

    1994-01-01

    Artificial intelligence is foreseen as the base for new control systems aimed to replace traditional controllers and to assist and eventually advise plant operators. This paper discusses the development of an indirect model reference adaptive control (MRAC) system, using the artificial neural network (ANN) technique, and its implementation to control the outlet steam temperature of a sodium to water evaporator. The ANN technique is applied in the identification and in the control process of the indirect MRAC system. The emphasis is placed on demonstrating the efficacy of the indirect MRAC system in controlling the outlet steam temperature of the evaporator, and on showing the important function covered by the ANN technique. An important characteristic of this control system is that it relays only on some selected input variables and output variables of the evaporator model. These are the variables that can be actually measured or calculated in a real environment. The results obtained applying the indirect MRAC system to control the evaporator model are quite remarkable. The outlet temperature of the steam is almost perfectly kept close to its desired set point, when the evaporator is forced to depart from steady state conditions, either due to the variation of some input variables or due to the alteration of some of its internal parameters. The results also show the importance of the role played by the ANN technique in the overall control action. The connecting weights of the ANN nodes self adjust to follow the modifications which may occur in the characteristic of the evaporator model during a transient. The efficiency and the accuracy of the control action highly depends on the on-line identification process of the ANN, which is responsible for upgrading the connecting weights of the ANN nodes. (J.P.N.)

  13. An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Foday Conteh

    2017-09-01

    Full Text Available In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

  14. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  15. An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

    International Nuclear Information System (INIS)

    Cai, Yuan; Wang, Jian-zhou; Tang, Yun; Yang, Yu-chen

    2011-01-01

    This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network. -- Research highlights: → The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. → The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. → The network on-line forecasts electrical load accurately, stably. → Both one-period and multi-period load forecasting are executed using data of different cities.

  16. Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

    Science.gov (United States)

    Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

    2011-04-01

    This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

  17. Cascading a systolic array and a feedforward neural network for navigation and obstacle avoidance using potential fields

    Science.gov (United States)

    Plumer, Edward S.

    1991-01-01

    A technique is developed for vehicle navigation and control in the presence of obstacles. A potential function was devised that peaks at the surface of obstacles and has its minimum at the proper vehicle destination. This function is computed using a systolic array and is guaranteed not to have local minima. A feedfoward neural network is then used to control the steering of the vehicle using local potential field information. In this case, the vehicle is a trailer truck backing up. Previous work has demonstrated the capability of a neural network to control steering of such a trailer truck backing to a loading platform, but without obstacles. Now, the neural network was able to learn to navigate a trailer truck around obstacles while backing toward its destination. The network is trained in an obstacle free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

  18. Evaluation of the Application of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems for Rainfall-Runoff Modelling in Zayandeh_rood Dam Basin

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Dastorani

    2012-01-01

    Full Text Available During recent few decades, due to the importance of the availability of water, and therefore the necesity of predicting run off resulted from rain fall there has been an increase in developing and implementation of new suitable method for prediction of run off using precipitation data. One of these approaches that have been developed in several areas of sciences including water related fields, is soft computing techniques such as artificial neural networks and fuzzy logic systems. This research was designed to evaluate the applicability of artificial neural network and adaptive neuro –fuzzy inference system to model rainfall-runoff process in Zayandeh_rood dam basin. It must be mentioned that, data have been analysed using Wingamma software, to select appropriate type and number of training input data before they can be used in the models. Then, it has been tried to evaluated applicability of artificial neural networks and neuro-fuzzy techniques to predict runoff generated from daily rainfall. Finally, the accuracy of the results produced by these methods has been compared using statistical criterion. Results taken from this research show that artificial neural networks and neuro-fuzzy technique presented different outputs in different conditions in terms of type and number of inputs variables, but both method have been able to produce acceptable results when suitable input variables and network structures are used.

  19. Adaptive Treatment Strategies in Youth Mental Health: A Commentary on Advantages, Challenges, and Potential Directions.

    Science.gov (United States)

    Sherrill, Joel T

    2016-01-01

    This commentary underscores the importance and potential of the research approaches and intervention strategies described in the JCCAP special issue on the Science of Adaptive Treatment Strategies in Child and Adolescent Mental Health for addressing the widely observed heterogeneity in response to even our most promising research-informed interventions. First, the commentary briefly summarizes the advantages of these approaches and highlights how these programs of research are responsive to widely agreed-upon calls for more personalized, prescriptive interventions. Next, the commentary briefly discusses key common challenges and gaps in our knowledge that might be addressed to advance the development, testing, and implementation of adaptive intervention strategies. For example, research to identify robust moderators that might serve as potential tailoring variables for initial assignment and sequencing of interventions, efforts to operationalize surrogate endpoints for early identification of individuals who are unlikely to respond to first-line interventions, and research that helps define what constitutes an adequate exposure (i.e., dose) or response threshold (e.g., response that suggests the need to intensify, switch, or augment interventions) would inform decision rules for adaptive algorithms. The commentary concludes with a discussion of potential strategies and current initiatives that might ultimately help facilitate research on more targeted, prescriptive approaches to intervening, including efforts to encourage investigators to use common data elements, to share and integrate data across trials, and to employ a more mechanism-based approach to intervention development and testing.

  20. Adapt

    Science.gov (United States)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  1. Neural induction with neurogenin 1 enhances the therapeutic potential of mesenchymal stem cells in an amyotrophic lateral sclerosis mouse model.

    Science.gov (United States)

    Chan-Il, Choi; Young-Don, Lee; Heejaung, Kim; Kim, Seung Hyun; Suh-Kim, Haeyoung; Kim, Sung-Soo

    2013-01-01

    Amyotrophic lateral sclerosis (ALS) is characterized by progressive dysfunction and degeneration of motor neurons in the central nervous system (CNS). In the absence of effective drug treatments for ALS, stem cell treatment has emerged as a candidate therapy for this disease. To date, however, there is no consensus protocol that stipulates stem cell types, transplantation timing, or frequency. Using an ALS mouse model carrying a high copy number of a mutant human superoxide dismutase-1 (SOD1)(G93A) transgene, we investigated the effect of neural induction on the innate therapeutic potential of mesenchymal stem cells (MSCs) in relation to preclinical transplantation parameters. In our study, the expression of monocyte chemoattractant protein-1 (MCP-1) was elevated in the ALS mouse spinal cord. Neural induction of MSCs with neurogenin 1 (Ngn1) upregulated the expression level of the MCP-1 receptor, CCR2, and enhanced the migration activity toward MCP-1 in vitro. Ngn1-expressing MSCs (MSCs-Ngn1) showed a corresponding increase in tropism to the CNS after systemic transplantation in ALS mice. Notably, MSCs-Ngn1 delayed disease onset if transplanted during preonset ages,whereas unprocessed MSCs failed to do so. If transplanted near the onset ages, a single treatment with MSCs-Ngn1 was sufficient to enhance motor functions during the symptomatic period (15–17 weeks), whereas unprocessed MSCs required repeated transplantation to achieve similar levels of motor function improvement. Our data indicate that systemically transplanted MSCs-Ngn1 can migrate to the CNS and exert beneficial effects on host neural cells for an extended period of time through paracrine functions, suggesting a potential benefit of neural induction of transplanted MSCs in long-term treatment of ALS.

  2. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

    Science.gov (United States)

    Nguyen, Thuong T.; Székely, Eszter; Imbalzano, Giulio; Behler, Jörg; Csányi, Gábor; Ceriotti, Michele; Götz, Andreas W.; Paesani, Francesco

    2018-06-01

    The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enables routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body (MB) expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials (PIPs), neural networks, and Gaussian approximation potentials (GAPs) in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, Behler-Parrinello neural network-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms.

  3. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region

    Science.gov (United States)

    He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

    2014-02-01

    Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

  4. Two projects in theoretical neuroscience: A convolution-based metric for neural membrane potentials and a combinatorial connectionist semantic network method

    Science.gov (United States)

    Evans, Garrett Nolan

    In this work, I present two projects that both contribute to the aim of discovering how intelligence manifests in the brain. The first project is a method for analyzing recorded neural signals, which takes the form of a convolution-based metric on neural membrane potential recordings. Relying only on integral and algebraic operations, the metric compares the timing and number of spikes within recordings as well as the recordings' subthreshold features: summarizing differences in these with a single "distance" between the recordings. Like van Rossum's (2001) metric for spike trains, the metric is based on a convolution operation that it performs on the input data. The kernel used for the convolution is carefully chosen such that it produces a desirable frequency space response and, unlike van Rossum's kernel, causes the metric to be first order both in differences between nearby spike times and in differences between same-time membrane potential values: an important trait. The second project is a combinatorial syntax method for connectionist semantic network encoding. Combinatorial syntax has been a point on which those who support a symbol-processing view of intelligent processing and those who favor a connectionist view have had difficulty seeing eye-to-eye. Symbol-processing theorists have persuasively argued that combinatorial syntax is necessary for certain intelligent mental operations, such as reasoning by analogy. Connectionists have focused on the versatility and adaptability offered by self-organizing networks of simple processing units. With this project, I show that there is a way to reconcile the two perspectives and to ascribe a combinatorial syntax to a connectionist network. The critical principle is to interpret nodes, or units, in the connectionist network as bound integrations of the interpretations for nodes that they share links with. Nodes need not correspond exactly to neurons and may correspond instead to distributed sets, or assemblies, of

  5. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  6. Recurrent myocardial infarction: Mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control

    Science.gov (United States)

    Ardell, Jeffrey L.; Shivkumar, Kalyanam; Armour, J. Andrew

    2017-01-01

    The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as ‘free-floating’ in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease. PMID:28692680

  7. Recurrent myocardial infarction: Mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control.

    Science.gov (United States)

    Kember, Guy; Ardell, Jeffrey L; Shivkumar, Kalyanam; Armour, J Andrew

    2017-01-01

    The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as 'free-floating' in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease.

  8. Recurrent myocardial infarction: Mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control.

    Directory of Open Access Journals (Sweden)

    Guy Kember

    Full Text Available The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as 'free-floating' in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease.

  9. Space-time adaptive decision feedback neural receivers with data selection for high-data-rate users in DS-CDMA systems.

    Science.gov (United States)

    de Lamare, Rodrigo C; Sampaio-Neto, Raimundo

    2008-11-01

    A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.

  10. Evaluating the adaptive potential of the European eel: is the immunogenetic status recovering?

    Directory of Open Access Journals (Sweden)

    Miguel Baltazar-Soares

    2016-04-01

    Full Text Available The recent increased integration of evolutionary theory into conservation programs has greatly improved our ability to protect endangered species. A common application of such theory links population dynamics and indices of genetic diversity, usually estimated from neutrally evolving markers. However, some studies have suggested that highly polymorphic adaptive genes, such as the immune genes of the Major Histocompatibility Complex (MHC, might be more sensitive to fluctuations in population dynamics. As such, the combination of neutrally- and adaptively-evolving genes may be informative in populations where reductions in abundance have been documented. The European eel (Anguilla anguilla underwent a drastic and well-reported decline in abundance in the late 20th century and still displays low recruitment. Here we compared genetic diversity indices estimated from neutral (mitochondrial DNA and microsatellites and adaptive markers (MHC between two distinct generations of European eels. Our results revealed a clear discrepancy between signatures obtained for each class of markers. Although mtDNA and microsatellites showed no changes in diversity between the older and the younger generations, MHC diversity revealed a contemporary drop followed by a recent increase. Our results suggest ongoing gain of MHC genetic diversity resulting from the interplay between drift and selection and ultimately increasing the adaptive potential of the species.

  11. Addressing potential local adaptation in species distribution models: implications for conservation under climate change

    Science.gov (United States)

    Hällfors, Maria Helena; Liao, Jishan; Dzurisin, Jason D. K.; Grundel, Ralph; Hyvärinen, Marko; Towle, Kevin; Wu, Grace C.; Hellmann, Jessica J.

    2016-01-01

    Species distribution models (SDMs) have been criticized for involving assumptions that ignore or categorize many ecologically relevant factors such as dispersal ability and biotic interactions. Another potential source of model error is the assumption that species are ecologically uniform in their climatic tolerances across their range. Typically, SDMs to treat a species as a single entity, although populations of many species differ due to local adaptation or other genetic differentiation. Not taking local adaptation into account, may lead to incorrect range prediction and therefore misplaced conservation efforts. A constraint is that we often do not know the degree to which populations are locally adapted, however. Lacking experimental evidence, we still can evaluate niche differentiation within a species' range to promote better conservation decisions. We explore possible conservation implications of making type I or type II errors in this context. For each of two species, we construct three separate MaxEnt models, one considering the species as a single population and two of disjunct populations. PCA analyses and response curves indicate different climate characteristics in the current environments of the populations. Model projections into future climates indicate minimal overlap between areas predicted to be climatically suitable by the whole species versus population-based models. We present a workflow for addressing uncertainty surrounding local adaptation in SDM application and illustrate the value of conducting population-based models to compare with whole-species models. These comparisons might result in more cautious management actions when alternative range outcomes are considered.

  12. Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization

    OpenAIRE

    Khulood A. Dagher; Ahmed S. Al-Araji

    2013-01-01

    A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation resu...

  13. Climate and water resource change impacts and adaptation potential for US power supply

    Science.gov (United States)

    Miara, Ariel; Macknick, Jordan E.; Vörösmarty, Charles J.; Tidwell, Vincent C.; Newmark, Robin; Fekete, Balazs

    2017-11-01

    Power plants that require cooling currently (2015) provide 85% of electricity generation in the United States. These facilities need large volumes of water and sufficiently cool temperatures for optimal operations, and projected climate conditions may lower their potential power output and affect reliability. We evaluate the performance of 1,080 thermoelectric plants across the contiguous US under future climates (2035-2064) and their collective performance at 19 North American Electric Reliability Corporation (NERC) sub-regions. Joint consideration of engineering interactions with climate, hydrology and environmental regulations reveals the region-specific performance of energy systems and the need for regional energy security and climate-water adaptation strategies. Despite climate-water constraints on individual plants, the current power supply infrastructure shows potential for adaptation to future climates by capitalizing on the size of regional power systems, grid configuration and improvements in thermal efficiencies. Without placing climate-water impacts on individual plants in a broader power systems context, vulnerability assessments that aim to support adaptation and resilience strategies misgauge the extent to which regional energy systems are vulnerable. Climate-water impacts can lower thermoelectric reserve margins, a measure of systems-level reliability, highlighting the need to integrate climate-water constraints on thermoelectric power supply into energy planning, risk assessments, and system reliability management.

  14. Comparative genomic analysis of Lactobacillus plantarum ZJ316 reveals its genetic adaptation and potential probiotic profiles.

    Science.gov (United States)

    Li, Ping; Li, Xuan; Gu, Qing; Lou, Xiu-Yu; Zhang, Xiao-Mei; Song, Da-Feng; Zhang, Chen

    2016-08-01

    In previous studies, Lactobacillus plantarum ZJ316 showed probiotic properties, such as antimicrobial activity against various pathogens and the capacity to significantly improve pig growth and pork quality. The purpose of this study was to reveal the genes potentially related to its genetic adaptation and probiotic profiles based on comparative genomic analysis. The genome sequence of L. plantarum ZJ316 was compared with those of eight L. plantarum strains deposited in GenBank. BLASTN, Mauve, and MUMmer programs were used for genome alignment and comparison. CRISPRFinder was applied for searching the clustered regularly interspaced short palindromic repeats (CRISPRs). We identified genes that encode proteins related to genetic adaptation and probiotic profiles, including carbohydrate transport and metabolism, proteolytic enzyme systems and amino acid biosynthesis, CRISPR adaptive immunity, stress responses, bile salt resistance, ability to adhere to the host intestinal wall, exopolysaccharide (EPS) biosynthesis, and bacteriocin biosynthesis. Comparative characterization of the L. plantarum ZJ316 genome provided the genetic basis for further elucidating the functional mechanisms of its probiotic properties. ZJ316 could be considered a potential probiotic candidate.

  15. Comparative genomic analysis of Lactobacillus plantarum ZJ316 reveals its genetic adaptation and potential probiotic profiles* #

    Science.gov (United States)

    Li, Ping; Li, Xuan; Gu, Qing; Lou, Xiu-yu; Zhang, Xiao-mei; Song, Da-feng; Zhang, Chen

    2016-01-01

    Objective: In previous studies, Lactobacillus plantarum ZJ316 showed probiotic properties, such as antimicrobial activity against various pathogens and the capacity to significantly improve pig growth and pork quality. The purpose of this study was to reveal the genes potentially related to its genetic adaptation and probiotic profiles based on comparative genomic analysis. Methods: The genome sequence of L. plantarum ZJ316 was compared with those of eight L. plantarum strains deposited in GenBank. BLASTN, Mauve, and MUMmer programs were used for genome alignment and comparison. CRISPRFinder was applied for searching the clustered regularly interspaced short palindromic repeats (CRISPRs). Results: We identified genes that encode proteins related to genetic adaptation and probiotic profiles, including carbohydrate transport and metabolism, proteolytic enzyme systems and amino acid biosynthesis, CRISPR adaptive immunity, stress responses, bile salt resistance, ability to adhere to the host intestinal wall, exopolysaccharide (EPS) biosynthesis, and bacteriocin biosynthesis. Conclusions: Comparative characterization of the L. plantarum ZJ316 genome provided the genetic basis for further elucidating the functional mechanisms of its probiotic properties. ZJ316 could be considered a potential probiotic candidate. PMID:27487802

  16. Emotional expectations influence neural sensitivity to fearful faces in humans:An event-related potential study

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The present study tested whether neural sensitivity to salient emotional facial expressions was influenced by emotional expectations induced by a cue that validly predicted the expression of a subsequently presented target face. Event-related potentials (ERPs) elicited by fearful and neutral faces were recorded while participants performed a gender discrimination task under cued (‘expected’) and uncued (‘unexpected’) conditions. The behavioral results revealed that accuracy was lower for fearful compared with neutral faces in the unexpected condition, while accuracy was similar for fearful and neutral faces in the expected condition. ERP data revealed increased amplitudes in the P2 component and 200–250 ms interval for unexpected fearful versus neutral faces. By contrast, ERP responses were similar for fearful and neutral faces in the expected condition. These findings indicate that human neural sensitivity to fearful faces is modulated by emotional expectations. Although the neural system is sensitive to unpredictable emotionally salient stimuli, sensitivity to salient stimuli is reduced when these stimuli are predictable.

  17. HDAC inhibition amplifies gap junction communication in neural progenitors: Potential for cell-mediated enzyme prodrug therapy

    International Nuclear Information System (INIS)

    Khan, Zahidul; Akhtar, Monira; Asklund, Thomas; Juliusson, Bengt; Almqvist, Per M.; Ekstroem, Tomas J.

    2007-01-01

    Enzyme prodrug therapy using neural progenitor cells (NPCs) as delivery vehicles has been applied in animal models of gliomas and relies on gap junction communication (GJC) between delivery and target cells. This study investigated the effects of histone deacetylase (HDAC) inhibitors on GJC for the purpose of facilitating transfer of therapeutic molecules from recombinant NPCs. We studied a novel immortalized midbrain cell line, NGC-407 of embryonic human origin having neural precursor characteristics, as a potential delivery vehicle. The expression of gap junction protein connexin 43 (C x 43) was analyzed by western blot and immunocytochemistry. While C x 43 levels were decreased in untreated differentiating NGC-407 cells, the HDAC inhibitor 4-phenylbutyrate (4-PB) increased C x 43 expression along with increased membranous deposition in both proliferating and differentiating cells. Simultaneously, Ser 279/282-phosphorylated form of C x 43 was declined in both culture conditions by 4-PB. The 4-PB effect in NGC-407 cells was verified by using HNSC.100 human neural progenitors and Trichostatin A. Improved functional GJC is of imperative importance for therapeutic strategies involving intercellular transport of low molecular-weight compounds. We show here an enhancement by 4-PB, of the functional GJC among NGC-407 cells, as well as between NGC-407 and human glioma cells, as indicated by increased fluorescent dye transfer

  18. Tolerance and potential for adaptation of a Baltic Sea rockweed under predicted climate change conditions.

    Science.gov (United States)

    Rugiu, Luca; Manninen, Iita; Rothäusler, Eva; Jormalainen, Veijo

    2018-03-01

    Climate change is threating species' persistence worldwide. To predict species responses to climate change we need information not just on their environmental tolerance but also on its adaptive potential. We tested how the foundation species of rocky littoral habitats, Fucus vesiculosus, responds to combined hyposalinity and warming projected to the Baltic Sea by 2070-2099. We quantified responses of replicated populations originating from the entrance, central, and marginal Baltic regions. Using replicated individuals, we tested for the presence of within-population tolerance variation. Future conditions hampered growth and survival of the central and marginal populations whereas the entrance populations fared well. Further, both the among- and within-population variation in responses to climate change indicated existence of genetic variation in tolerance. Such standing genetic variation provides the raw material necessary for adaptation to a changing environment, which may eventually ensure the persistence of the species in the inner Baltic Sea. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Yimin [School of Mathematics and Statistics, Suzhou University, Suzhou 234000 (China); Lv, Hui, E-mail: lvhui207@gmail.com [Department of Applied Mathematics, Huainan Normal University, Huainan 232038 (China)

    2016-08-15

    In this paper, we consider the control problem of a class of uncertain fractional-order chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results.

  20. High resolution crop growth simulation for identification of potential adaptation strategies under climate change

    Science.gov (United States)

    Kim, K. S.; Yoo, B. H.

    2016-12-01

    Impact assessment of climate change on crop production would facilitate planning of adaptation strategies. Because socio-environmental conditions would differ by local areas, it would be advantageous to assess potential adaptation measures at a specific area. The objectives of this study was to develop a crop growth simulation system at a very high spatial resolution, e.g., 30 m, and to assess different adaptation options including shift of planting date and use of different cultivars. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to predict yields of soybean and maize in Korea. Gridded data for climate and soil were used to prepare input data for the DSSAT model. Weather input data were prepared at the resolution of 30 m using bilinear interpolation from gridded climate scenario data. Those climate data were obtained from Korean Meteorology Administration. Spatial resolution of temperature and precipitation was 1 km whereas that of solar radiation was 12.5 km. Soil series data at the 30 m resolution were obtained from the soil database operated by Rural Development Administration, Korea. The SOL file, which is a soil input file for the DSSAT model was prepared using physical and chemical properties of a given soil series, which were available from the soil database. Crop yields were predicted by potential adaptation options based on planting date and cultivar. For example, 10 planting dates and three cultivars were used to identify ideal management options for climate change adaptation. In prediction of maize yield, combination of 20 planting dates and two cultivars was used as management options. Predicted crop yields differed by site even within a relatively small region. For example, the maximum of average yields for 2001-2010 seasons differed by sites In a county of which areas is 520 km2 (Fig. 1). There was also spatial variation in the ideal management option in the region (Fig. 2). These results suggested that local

  1. Potential economic benefits of adapting agricultural production systems to future climate change

    Science.gov (United States)

    Fagre, Daniel B.; Pederson, Gregory; Bengtson, Lindsey E.; Prato, Tony; Qui, Zeyuan; Williams, Jimmie R.

    2010-01-01

    Potential economic impacts of future climate change on crop enterprise net returns and annual net farm income (NFI) are evaluated for small and large representative farms in Flathead Valley in Northwest Montana. Crop enterprise net returns and NFI in an historical climate period (1960–2005) and future climate period (2006–2050) are compared when agricultural production systems (APSs) are adapted to future climate change. Climate conditions in the future climate period are based on the A1B, B1, and A2 CO2 emission scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report. Steps in the evaluation include: (1) specifying crop enterprises and APSs (i.e., combinations of crop enterprises) in consultation with locals producers; (2) simulating crop yields for two soils, crop prices, crop enterprises costs, and NFIs for APSs; (3) determining the dominant APS in the historical and future climate periods in terms of NFI; and (4) determining whether NFI for the dominant APS in the historical climate period is superior to NFI for the dominant APS in the future climate period. Crop yields are simulated using the Environmental/Policy Integrated Climate (EPIC) model and dominance comparisons for NFI are based on the stochastic efficiency with respect to a function (SERF) criterion. Probability distributions that best fit the EPIC-simulated crop yields are used to simulate 100 values for crop yields for the two soils in the historical and future climate periods. Best-fitting probability distributions for historical inflation-adjusted crop prices and specified triangular probability distributions for crop enterprise costs are used to simulate 100 values for crop prices and crop enterprise costs. Averaged over all crop enterprises, farm sizes, and soil types, simulated net return per ha averaged over all crop enterprises decreased 24% and simulated mean NFI for APSs decreased 57% between the historical and future climate periods. Although adapting

  2. Potential Economic Benefits of Adapting Agricultural Production Systems to Future Climate Change

    Science.gov (United States)

    Prato, Tony; Zeyuan, Qiu; Pederson, Gregory; Fagre, Dan; Bengtson, Lindsey E.; Williams, Jimmy R.

    2010-03-01

    Potential economic impacts of future climate change on crop enterprise net returns and annual net farm income (NFI) are evaluated for small and large representative farms in Flathead Valley in Northwest Montana. Crop enterprise net returns and NFI in an historical climate period (1960-2005) and future climate period (2006-2050) are compared when agricultural production systems (APSs) are adapted to future climate change. Climate conditions in the future climate period are based on the A1B, B1, and A2 CO2 emission scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report. Steps in the evaluation include: (1) specifying crop enterprises and APSs (i.e., combinations of crop enterprises) in consultation with locals producers; (2) simulating crop yields for two soils, crop prices, crop enterprises costs, and NFIs for APSs; (3) determining the dominant APS in the historical and future climate periods in terms of NFI; and (4) determining whether NFI for the dominant APS in the historical climate period is superior to NFI for the dominant APS in the future climate period. Crop yields are simulated using the Environmental/Policy Integrated Climate (EPIC) model and dominance comparisons for NFI are based on the stochastic efficiency with respect to a function (SERF) criterion. Probability distributions that best fit the EPIC-simulated crop yields are used to simulate 100 values for crop yields for the two soils in the historical and future climate periods. Best-fitting probability distributions for historical inflation-adjusted crop prices and specified triangular probability distributions for crop enterprise costs are used to simulate 100 values for crop prices and crop enterprise costs. Averaged over all crop enterprises, farm sizes, and soil types, simulated net return per ha averaged over all crop enterprises decreased 24% and simulated mean NFI for APSs decreased 57% between the historical and future climate periods. Although adapting APSs to

  3. Potential economic benefits of adapting agricultural production systems to future climate change.

    Science.gov (United States)

    Prato, Tony; Zeyuan, Qiu; Pederson, Gregory; Fagre, Dan; Bengtson, Lindsey E; Williams, Jimmy R

    2010-03-01

    Potential economic impacts of future climate change on crop enterprise net returns and annual net farm income (NFI) are evaluated for small and large representative farms in Flathead Valley in Northwest Montana. Crop enterprise net returns and NFI in an historical climate period (1960-2005) and future climate period (2006-2050) are compared when agricultural production systems (APSs) are adapted to future climate change. Climate conditions in the future climate period are based on the A1B, B1, and A2 CO(2) emission scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report. Steps in the evaluation include: (1) specifying crop enterprises and APSs (i.e., combinations of crop enterprises) in consultation with locals producers; (2) simulating crop yields for two soils, crop prices, crop enterprises costs, and NFIs for APSs; (3) determining the dominant APS in the historical and future climate periods in terms of NFI; and (4) determining whether NFI for the dominant APS in the historical climate period is superior to NFI for the dominant APS in the future climate period. Crop yields are simulated using the Environmental/Policy Integrated Climate (EPIC) model and dominance comparisons for NFI are based on the stochastic efficiency with respect to a function (SERF) criterion. Probability distributions that best fit the EPIC-simulated crop yields are used to simulate 100 values for crop yields for the two soils in the historical and future climate periods. Best-fitting probability distributions for historical inflation-adjusted crop prices and specified triangular probability distributions for crop enterprise costs are used to simulate 100 values for crop prices and crop enterprise costs. Averaged over all crop enterprises, farm sizes, and soil types, simulated net return per ha averaged over all crop enterprises decreased 24% and simulated mean NFI for APSs decreased 57% between the historical and future climate periods. Although adapting APSs

  4. Ischemic long-term-potentiation (iLTP: perspectives to set the threshold of neural plasticity toward therapy

    Directory of Open Access Journals (Sweden)

    Maximilian Lenz

    2015-01-01

    Full Text Available The precise role of neural plasticity under pathological conditions remains not well understood. It appears to be well accepted, however, that changes in the ability of neurons to express plasticity accompany neurological diseases. Here, we discuss recent experimental evidence, which suggests that synaptic plasticity induced by a pathological stimulus, i.e., ischemic long-term-potentiation (iLTP of excitatory synapses, could play an important role for post-stroke recovery by influencing the post-lesional reorganization of surviving neuronal networks.

  5. Relationships Between Vestibular Measures as Potential Predictors for Spaceflight Sensorimotor Adaptation

    Science.gov (United States)

    Clark, T. K.; Peters, B.; Gadd, N. E.; De Dios, Y. E.; Wood, S.; Bloomberg, J. J.; Mulavara, A. P.

    2016-01-01

    Introduction: During space exploration missions astronauts are exposed to a series of novel sensorimotor environments, requiring sensorimotor adaptation. Until adaptation is complete, sensorimotor decrements occur, affecting critical tasks such as piloted landing or docking. Of particularly interest are locomotion tasks such as emergency vehicle egress or extra-vehicular activity. While nearly all astronauts eventually adapt sufficiently, it appears there are substantial individual differences in how quickly and effectively this adaptation occurs. These individual differences in capacity for sensorimotor adaptation are poorly understood. Broadly, we aim to identify measures that may serve as pre-flight predictors of and individual's adaptation capacity to spaceflight-induced sensorimotor changes. As a first step, since spaceflight is thought to involve a reinterpretation of graviceptor cues (e.g. otolith cues from the vestibular system) we investigate the relationships between various measures of vestibular function in humans. Methods: In a set of 15 ground-based control subjects, we quantified individual differences in vestibular function using three measures: 1) ocular vestibular evoked myogenic potential (oVEMP), 2) computerized dynamic posturography and 3) vestibular perceptual thresholds. oVEMP responses are elicited using a mechanical stimuli approach. Computerized dynamic posturography was used to quantify Sensory Organization Tests (SOTs), including SOT5M which involved performing pitching head movements while balancing on a sway-reference support surface with eyes closed. We implemented a vestibular perceptual threshold task using the tilt capabilities of the Tilt-Translation Sled (TTS) at JSC. On each trial, the subject was passively roll-tilted left ear down or right ear down in the dark and verbally provided a forced-choice response regarding which direction they felt tilted. The motion profile was a single-cycle sinusoid of angular acceleration with a

  6. Mixed H∞ and passive projective synchronization for fractional-order memristor-based neural networks with time delays via adaptive sliding mode control

    Science.gov (United States)

    Song, Shuai; Song, Xiaona; Balsera, Ines Tejado

    2017-05-01

    This paper investigates the mixed H∞ and passive projective synchronization problem for fractional-order (FO) memristor-based neural networks with time delays. Our aim is to design a controller such that, though the unavoidable phenomena of time delay and external disturbances is fully considered, the resulting closed-loop system is stable with a mixed H∞ and passive performance level. By combining sliding mode control and adaptive control methods, a novel adaptive sliding mode control strategy is designed for the synchronization of time-delayed FO dynamic networks. Via the application of FO system stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequalities. Based on the conditions, a desired controller which can guarantee the stability of the closed-loop system and also ensure a mixed H∞ and passive performance level is designed. Finally, two simulation examples are given to illustrate the effectiveness of the proposed method.

  7. Potential of cold-adapted microorganisms for bioremediation of oil-polluted Alpine soils

    International Nuclear Information System (INIS)

    Margesin, R.

    2000-01-01

    The environmental contamination by organic pollutants is a widespread problem in all climates. The most widely distributed pollution can be attributed to oil contamination. Bioremediation methods can provide efficient, inexpensive and environmentally safe cleanup tools. The role of cold-adapted microorganisms for the bioremediation of experimentally and chronically oil-contaminated Alpine soils was evaluated in the studies described. The results demonstrated that there is a considerable potential for oil bioremediation in Alpine soils. Oil biodegradation can be significantly enhanced by biostimulation (inorganic nutrient supply), but a complete oil elimination is not possible by employing biological decontamination alone. (Author)

  8. Potential psychological & neural mechanisms in binge eating disorder: Implications for treatment.

    Science.gov (United States)

    Kober, Hedy; Boswell, Rebecca G

    2018-03-01

    Binge Eating Disorder (BED) is a newly-established eating disorder diagnosis in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Although systematic research on BED is in its infancy and many studies feature small samples, several observations emerge. First, we review diagnostic, developmental, and socio-demographic features of BED. Next, although BED and obesity are linked and frequently co-occur, we review data suggesting that BED is a distinct phenotype. Importantly, we take a mechanism-focused approach and propose four psychological processes with neurobiological bases that may uniquely differentiate BED from obesity: emotion reactivity, food-cue reactivity, food craving, and cognitive control. Further, we propose that interactions between impairments in cognitive control and increased emotional reactivity, food-cue reactivity, and craving may underlie emotion dysregulation and promote binge eating. Consistently, neuroimaging studies point towards neural alterations in the response to rewards and to food specifically, and suggest preliminary links between impaired cognitive-control-related neural activity and binge eating. However, additional systematic work is required in this area. We conclude with a detailed review of treatment approaches to BED; specifically, we suggest that psychological and pharmacological treatments that target core mechanisms - including cognitive control and emotion/craving dysregulation - may be particularly effective. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Fetal Alcohol Spectrum Disorder (FASD) Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets.

    Science.gov (United States)

    Muralidharan, Pooja; Sarmah, Swapnalee; Zhou, Feng C; Marrs, James A

    2013-06-19

    Fetal alcohol spectrum disorder (FASD), caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  10. Fetal Alcohol Spectrum Disorder (FASD Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets

    Directory of Open Access Journals (Sweden)

    James A. Marrs

    2013-06-01

    Full Text Available Fetal alcohol spectrum disorder (FASD, caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  11. Specific neural traces for intonational discourse categories as revealed by human-evoked potentials.

    Science.gov (United States)

    Borràs-Comes, Joan; Costa-Faidella, Jordi; Prieto, Pilar; Escera, Carles

    2012-04-01

    The neural representation of segmental and tonal phonological distinctions has been shown by means of the MMN ERP, yet this is not the case for intonational discourse contrasts. In Catalan, a rising-falling intonational sequence can be perceived as a statement or as a counterexpectational question, depending exclusively on the size of the pitch range interval of the rising movement. We tested here, using the MMN, whether such categorical distinctions elicited distinct neurophysiological patterns of activity, supporting their specific neural representation. From a behavioral identification experiment, we set the boundary between the two categories and defined four stimuli across the continuum. Although the physical distance between each pair of stimuli was kept constant, the central pair represented an across-category contrast, whereas the other pairs represented within-category contrasts. These four auditory stimuli were contrasted by pairs in three different oddball blocks. The mean amplitude of the MMN was larger for the across-category contrast, suggesting that intonational contrasts in the target language can be encoded automatically in the auditory cortex. These results are in line with recent findings in other fields of linguistics, showing that, when a boundary between categories is crossed, the MMN response is not just larger but rather includes a separate subcomponent.

  12. Fast and accurate solution for the SCUC problem in large-scale power systems using adapted binary programming and enhanced dual neural network

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M.P.; Sheikh-El-Eslami, M.K.; Catalão, J.P.S.

    2014-01-01

    Highlights: • A novel hybrid method based on decomposition of SCUC into QP and BP problems is proposed. • An adapted binary programming and an enhanced dual neural network model are applied. • The proposed EDNN is exactly convergent to the global optimal solution of QP. • An AC power flow procedure is developed for including contingency/security issues. • It is suited for large-scale systems, providing both accurate and fast solutions. - Abstract: This paper presents a novel hybrid method for solving the security constrained unit commitment (SCUC) problem. The proposed formulation requires much less computation time in comparison with other methods while assuring the accuracy of the results. Furthermore, the framework provided here allows including an accurate description of warmth-dependent startup costs, valve point effects, multiple fuel costs, forbidden zones of operation, and AC load flow bounds. To solve the nonconvex problem, an adapted binary programming method and enhanced dual neural network model are utilized as optimization tools, and a procedure for AC power flow modeling is developed for including contingency/security issues, as new contributions to earlier studies. Unlike classical SCUC methods, the proposed method allows to simultaneously solve the unit commitment problem and comply with the network limits. In addition to conventional test systems, a real-world large-scale power system with 493 units has been used to fully validate the effectiveness of the novel hybrid method proposed

  13. Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

    Science.gov (United States)

    Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.; McKee, Dan

    2001-07-01

    A new neural network technology was developed for improving the benign/malignant diagnosis of breast cancer using mammogram findings. A new paradigm, Adaptive Boosting (AB), uses a markedly different theory in solutioning Computational Intelligence (CI) problems. AB, a new machine learning paradigm, focuses on finding weak learning algorithm(s) that initially need to provide slightly better than random performance (i.e., approximately 55%) when processing a mammogram training set. Then, by successive development of additional architectures (using the mammogram training set), the adaptive boosting process improves the performance of the basic Evolutionary Programming derived neural network architectures. The results of these several EP-derived hybrid architectures are then intelligently combined and tested using a similar validation mammogram data set. Optimization focused on improving specificity and positive predictive value at very high sensitivities, where an analysis of the performance of the hybrid would be most meaningful. Using the DUKE mammogram database of 500 biopsy proven samples, on average this hybrid was able to achieve (under statistical 5-fold cross-validation) a specificity of 48.3% and a positive predictive value (PPV) of 51.8% while maintaining 100% sensitivity. At 97% sensitivity, a specificity of 56.6% and a PPV of 55.8% were obtained.

  14. An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances.

    Science.gov (United States)

    Fairbank, Michael; Li, Shuhui; Fu, Xingang; Alonso, Eduardo; Wunsch, Donald

    2014-01-01

    We present a recurrent neural-network (RNN) controller designed to solve the tracking problem for control systems. We demonstrate that a major difficulty in training any RNN is the problem of exploding gradients, and we propose a solution to this in the case of tracking problems, by introducing a stabilization matrix and by using carefully constrained context units. This solution allows us to achieve consistently lower training errors, and hence allows us to more easily introduce adaptive capabilities. The resulting RNN is one that has been trained off-line to be rapidly adaptive to changing plant conditions and changing tracking targets. The case study we use is a renewable-energy generator application; that of producing an efficient controller for a three-phase grid-connected converter. The controller we produce can cope with the random variation of system parameters and fluctuating grid voltages. It produces tracking control with almost instantaneous response to changing reference states, and virtually zero oscillation. This compares very favorably to the classical proportional integrator (PI) controllers, which we show produce a much slower response and settling time. In addition, the RNN we propose exhibits better learning stability and convergence properties, and can exhibit faster adaptation, than has been achieved with adaptive critic designs. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Quantifying variety-specific heat resistance and the potential for adaptation to climate change.

    Science.gov (United States)

    Tack, Jesse; Barkley, Andrew; Rife, Trevor W; Poland, Jesse A; Nalley, Lawton Lanier

    2016-08-01

    The impact of climate change on crop yields has become widely measured; however, the linkages for winter wheat are less studied due to dramatic weather changes during the long growing season that are difficult to model. Recent research suggests significant reductions under warming. A potential adaptation strategy involves the development of heat resistant varieties by breeders, combined with alternative variety selection by producers. However, the impact of heat on specific wheat varieties remains relatively unstudied due to limited data and the complex genetic basis of heat tolerance. Here, we provide a novel econometric approach that combines field-trial data with a genetic cluster mapping to group wheat varieties and estimate a separate extreme heat impact (temperatures over 34 °C) across 24 clusters spanning 197 varieties. We find a wide range of heterogeneous heat resistance and a trade-off between average yield and resistance. Results suggest that recently released varieties are less heat resistant than older varieties, a pattern that also holds for on-farm varieties. Currently released - but not yet adopted - varieties do not offer improved resistance relative to varieties currently grown on farm. Our findings suggest that warming impacts could be significantly reduced through advances in wheat breeding and/or adoption decisions by producers. However, current adaptation-through-adoption potential is limited under a 1 °C warming scenario as increased heat resistance cannot be achieved without a reduction in average yields. © 2015 John Wiley & Sons Ltd.

  16. Adaptive Forming of the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Janusz Dudczyk

    2012-01-01

    Full Text Available Microstrip antenna has been recently one of the most innovative fields of antenna techniques. The main advantage of such an antenna is the simplicity of its production, little weight, a narrow profile, and easiness of integration of the radiating elements with the net of generators power systems. As a result of using arrays consisting of microstrip antennas; it is possible to decrease the size and weight and also to reduce the costs of components production as well as whole application systems. This paper presents possibilities of using artificial neural networks (ANNs in the process of forming a beam from radiating complex microstrip antenna. Algorithms which base on artificial neural networks use high parallelism of actions which results in considerable acceleration of the process of forming the antenna pattern. The appropriate selection of learning constants makes it possible to get theoretically a solution which will be close to the real time. This paper presents the training neural network algorithm with the selection of optimal network structure. The analysis above was made in case of following the emission source, setting to zero the pattern of direction of expecting interference, and following emission source compared with two constant interferences. Computer simulation was made in MATLAB environment on the basis of Flex Tool, a programme which creates artificial neural networks.

  17. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

    Directory of Open Access Journals (Sweden)

    Denis Delisle-Rodriguez

    2017-11-01

    Full Text Available This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs based on Canonical Correlation Analysis (CCA to recognize 40 targets of steady-state visual evoked potential (SSVEP, providing an accuracy (ACC of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly ( p < 0.01 improved for most of the subjects ( A C C ≥ 74.79 % , when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry.

  18. Geobacter sulfurreducens adapts to low electrode potential for extracellular electron transfer

    International Nuclear Information System (INIS)

    Peng, Luo; Zhang, Xiao-Ting; Yin, Jie; Xu, Shuo-Yuan; Zhang, Yong; Xie, De-Ti; Li, Zhen-Lun

    2016-01-01

    Microbial extracellular electron transfer (EET) occurring in natural and engineering processes is attracting increasing interests. While a meaningful question for bioenergetics, microbial physiology and microbial electrochemical systems; less is known about the lower limit of electron acceptor reduction potential for EET. It is also unclear how microbes adapt to weak electron acceptors. This study evaluated Geobacter sulfurreducens biofilms grown with an electrode poised at −0.25 V vs. SHE. This potential was found to be sufficient for microbial metabolism and proliferation. The turnover cyclic voltammetries found that these biofilms had a half-saturation potential of −0.242 ± 0.004 V, in contrast to −0.151 ± 0.003 V for that of the biofilms grown under 0.2 V. For the biofilms grown under 0.2 V, differential pulse voltammetry showed that the metabolic current was mediated by interfacial cofactors with mid-point potential around −0.16 V performing single-electron electron transfer (ET). The major electron conduits for the biofilms respiring under −0.25 V had mid-point potentials of −0.22 V or −0.26 V, which appeared to perform two-electron ET. Under the non-turnover condition, both biofilms showed similar patterns in voltammograms and the low-potential conduits largely disappeared for the biofilms grown under −0.25 V. Transcriptome analysis identified 17 cytochrome-c genes significantly up-regulated for the biofilms grown under −0.25 V, together with many other genes linked to the ET system. It was also noted that, lowering the poised potential from −0.25 V to −0.28 V (the fuel standard oxidation potential) did not fully inhibit microbial respiration.

  19. The Intrauterine Growth Restriction Phenotype: Fetal Adaptations and Potential Implications for Later Life Insulin Resistance and Diabetes

    Science.gov (United States)

    Thorn, Stephanie R.; Rozance, Paul J.; Brown, Laura D.; Hay, William W.

    2011-01-01

    The intrauterine growth restricted (IUGR) fetus develops unique metabolic adaptations in response to exposure to reduced nutrient supply. These adaptations provide survival value for the fetus by enhancing the capacity of the fetus to take up and use nutrients, thereby reducing the need for nutrient supply. Each organ and tissue in the fetus adapts differently, with the brain showing the greatest capacity for maintaining nutrient supply and growth. Such adaptations, if persistent, also have the potential in later life to promote nutrient uptake and storage, which directly lead to complications of obesity, insulin resistance, reduced insulin production, and type 2 diabetes. PMID:21710398

  20. Zika Virus Strains Potentially Display Different Infectious Profiles in Human Neural Cells

    Directory of Open Access Journals (Sweden)

    Yannick Simonin

    2016-10-01

    Full Text Available The recent Zika virus (ZIKV epidemic has highlighted the poor knowledge on its physiopathology. Recent studies showed that ZIKV of the Asian lineage, responsible for this international outbreak, causes neuropathology in vitro and in vivo. However, two African lineages exist and the virus is currently found circulating in Africa. The original African strain was also suggested to be neurovirulent but its laboratory usage has been criticized due to its multiple passages. In this study, we compared the French Polynesian (Asian ZIKV strain to an African strain isolated in Central African Republic and show a difference in infectivity and cellular response between both strains in human neural stem cells and astrocytes. Consistently, this African strain led to a higher infection rate and viral production, as well as stronger cell death and anti-viral response. Our results highlight the need to better characterize the physiopathology and predict neurological impairment associated with African ZIKV.

  1. Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems

    DEFF Research Database (Denmark)

    Kolbæk, Morten; Tan, Zheng-Hua; Jensen, Jesper

    2017-01-01

    In this paper, we study aspects of single microphone speech enhancement (SE) based on deep neural networks (DNNs). Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker...... general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error (STSA-MMSE) based SE algorithm. We show that DNN-based SE systems, when trained...... a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI...

  2. Particularities of pathogenic microorganism development at anthropogenic influence and estimate of their adaptation potential by means of radiobiological method

    International Nuclear Information System (INIS)

    Shilina, Yu.V.; Gushcha, N.I.; Dyachenko, A.I.; Dmitriev, A.P.; Molozhava, O.S.; Romashko, V.M.

    2008-01-01

    Influence of anthropogenic factors on ecosystems causes their structure disturbance and reduction of species variety. Some resistance nonspecific forms of pathogenic microorganisms, which have high adaptation potential, become dominant. Thus their aggressiveness can increase. (authors)

  3. A neural basis for the visual sense of number and its development: A steady-state visual evoked potential study in children and adults

    Directory of Open Access Journals (Sweden)

    Joonkoo Park

    2018-04-01

    Full Text Available While recent studies in adults have demonstrated the existence of a neural mechanism for a visual sense of number, little is known about its development and whether such a mechanism exists at young ages. In the current study, I introduce a novel steady-state visual evoked potential (SSVEP technique to objectively quantify early visual cortical sensitivity to numerical and non-numerical magnitudes of a dot array. I then examine this neural sensitivity to numerical magnitude in children between three and ten years of age and in college students. Children overall exhibit strong SSVEP sensitivity to numerical magnitude in the right occipital sites with negligible SSVEP sensitivity to non-numerical magnitudes, the pattern similar to what is observed in adults. However, a closer examination of age differences reveals that this selective neural sensitivity to numerical magnitude, which is close to absent in three-year-olds, increases steadily as a function of age, while there is virtually no neural sensitivity to other non-numerical magnitudes across these ages. These results demonstrate the emergence of a neural mechanism underlying direct perception of numerosity across early and middle childhood and provide a potential neural mechanistic explanation for the development of humans’ primitive, non-verbal ability to comprehend number. Keywords: Numerosity, Steady-state visual evoked potential, Child development, Visual cortex, Approximate number system

  4. Bmps and id2a act upstream of Twist1 to restrict ectomesenchyme potential of the cranial neural crest.

    Directory of Open Access Journals (Sweden)

    Ankita Das

    Full Text Available Cranial neural crest cells (CNCCs have the remarkable capacity to generate both the non-ectomesenchyme derivatives of the peripheral nervous system and the ectomesenchyme precursors of the vertebrate head skeleton, yet how these divergent lineages are specified is not well understood. Whereas studies in mouse have indicated that the Twist1 transcription factor is important for ectomesenchyme development, its role and regulation during CNCC lineage decisions have remained unclear. Here we show that two Twist1 genes play an essential role in promoting ectomesenchyme at the expense of non-ectomesenchyme gene expression in zebrafish. Twist1 does so by promoting Fgf signaling, as well as potentially directly activating fli1a expression through a conserved ectomesenchyme-specific enhancer. We also show that Id2a restricts Twist1 activity to the ectomesenchyme lineage, with Bmp activity preferentially inducing id2a expression in non-ectomesenchyme precursors. We therefore propose that the ventral migration of CNCCs away from a source of Bmps in the dorsal ectoderm promotes ectomesenchyme development by relieving Id2a-dependent repression of Twist1 function. Together our model shows how the integration of Bmp inhibition at its origin and Fgf activation along its migratory route would confer temporal and spatial specificity to the generation of ectomesenchyme from the neural crest.

  5. A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

    2016-01-01

    Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior...

  6. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2005-03-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  7. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2008-11-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  8. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  9. Adolescent transformations of behavioral and neural processes as potential targets for prevention.

    Science.gov (United States)

    Eldreth, Dana; Hardin, Michael G; Pavletic, Nevia; Ernst, Monique

    2013-06-01

    Adolescence is a transitional period in development that is marked by a distinct, typical behavioral profile of high rates of exploration, novelty-seeking, and emotional lability. While these behaviors generally assist the adolescent transition to independence, they can also confer vulnerability for excessive risk-taking and psychopathology, particularly in the context of specific environmental or genetic influences. As prevention research depends on the identification of targets of vulnerability, the following review will discuss the interplay among motivational systems including reward-related, avoidance-related, and regulatory processes in typical and atypical adolescent development. Each set of processes will be discussed in relation to their underlying neural correlates and distinct developmental trajectories. Evidence suggests that typical adolescent behavior and the risk for atypical development are mediated by heightened adolescent responsiveness of reward-related and avoidance-related systems under specific conditions, concurrent with poor modulation by immature regulatory processes. Finally, we will propose strategies to exploit heightened reward processing to reinforce inhibitory control, which is an essential component of regulatory processes in prevention interventions.

  10. Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation

    Science.gov (United States)

    Dittbrenner, Benjamin J.; Pollack, Michael M.; Schilling, Jason W.; Olden, Julian D.; Lawler, Joshua J.; Torgersen, Christian E.

    2018-01-01

    Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors—information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17

  11. Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation.

    Science.gov (United States)

    Dittbrenner, Benjamin J; Pollock, Michael M; Schilling, Jason W; Olden, Julian D; Lawler, Joshua J; Torgersen, Christian E

    2018-01-01

    Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors-information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are

  12. An integral term adaptive neural control of fed-batch fermentation biotechnological process; Control neuronal adaptable con termino integral para un proceso biotecnologico de fermentacion por lote alimentado

    Energy Technology Data Exchange (ETDEWEB)

    Baruch, Ieroham; Hernandez, Luis Alberto; Barrera Cortes, Josefina [Centro de Investigacion y de Estudios Avanzados, Instituto Politecnico Nacional, Mexico D.F. (Mexico)

    2005-07-15

    A nonlinear mathematical model of aerobic biotechnological process of a fed-batch fermentation system is derived using ordinary differential equations. A neurocontrol is applied using Recurrent Trainable Neural Network (RTNN) plus integral term; the first network performs an approximation of the plant's output; the second network generates the control signal so that the biomass concentration could be regulated by the nutrient influent flow rate into the bioreactor. [Spanish] Un modelo matematico no lineal de un proceso biotecnologico aerobio de un sistema de fermentacion por lote alimentado es presentado mediante ecuaciones diferenciales ordinarias. Es propuesto un control utilizando dos redes neuronales recurrentes entrenables (RNRE) con la adicion de un termino integral; la primera red representa un aproximador de la salida de la planta y la segunda genera la senal de control tal que la concentracion de la biomasa pueda ser regulada mediante la alimentacion de un flujo con nutrientes al biorreactor.

  13. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  14. Health economic modeling of the potential cost saving effects of Neurally Adjusted Ventilator Assist.

    Science.gov (United States)

    Hjelmgren, Jonas; Bruce Wirta, Sara; Huetson, Pernilla; Myrén, Karl-Johan; Göthberg, Sylvia

    2016-02-01

    Asynchrony between patient and ventilator breaths is associated with increased duration of mechanical ventilation (MV). Neurally Adjusted Ventilatory Assist (NAVA) controls MV through an esophageal reading of diaphragm electrical activity via a nasogastric tube mounted with electrode rings. NAVA has been shown to decrease asynchrony in comparison to pressure support ventilation (PSV). The objective of this study was to conduct a health economic evaluation of NAVA compared with PSV. We developed a model based on an indirect link between improved synchrony with NAVA versus PSV and fewer days spent on MV in synchronous patients. Unit costs for MV were obtained from the Swedish intensive care unit register, and used in the model along with NAVA-specific costs. The importance of each parameter (proportion of asynchronous patients, costs, and average MV duration) for the overall results was evaluated through sensitivity analyses. Base case results showed that 21% of patients ventilated with NAVA were asynchronous versus 52% of patients receiving PSV. This equals an absolute difference of 31% and an average of 1.7 days less on MV and a total cost saving of US$7886 (including NAVA catheter costs). A breakeven analysis suggested that NAVA was cost effective compared with PSV given an absolute difference in the proportion of asynchronous patients greater than 2.5% (49.5% versus 52% asynchronous patients with NAVA and PSV, respectively). The base case results were stable to changes in parameters, such as difference in asynchrony, duration of ventilation and daily intensive care unit costs. This study showed economically favorable results for NAVA versus PSV. Our results show that only a minor decrease in the proportion of asynchronous patients with NAVA is needed for investments to pay off and generate savings. Future studies need to confirm this result by directly relating improved synchrony to the number of days on MV. © The Author(s), 2015.

  15. Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

    Science.gov (United States)

    Pukrittayakamee, A.; Malshe, M.; Hagan, M.; Raff, L. M.; Narulkar, R.; Bukkapatnum, S.; Komanduri, R.

    2009-04-01

    An improved neural network (NN) approach is presented for the simultaneous development of accurate potential-energy hypersurfaces and corresponding force fields that can be utilized to conduct ab initio molecular dynamics and Monte Carlo studies on gas-phase chemical reactions. The method is termed as combined function derivative approximation (CFDA). The novelty of the CFDA method lies in the fact that although the NN has only a single output neuron that represents potential energy, the network is trained in such a way that the derivatives of the NN output match the gradient of the potential-energy hypersurface. Accurate force fields can therefore be computed simply by differentiating the network. Both the computed energies and the gradients are then accurately interpolated using the NN. This approach is superior to having the gradients appear in the output layer of the NN because it greatly simplifies the required architecture of the network. The CFDA permits weighting of function fitting relative to gradient fitting. In every test that we have run on six different systems, CFDA training (without a validation set) has produced smaller out-of-sample testing error than early stopping (with a validation set) or Bayesian regularization (without a validation set). This indicates that CFDA training does a better job of preventing overfitting than the standard methods currently in use. The training data can be obtained using an empirical potential surface or any ab initio method. The accuracy and interpolation power of the method have been tested for the reaction dynamics of H+HBr using an analytical potential. The results show that the present NN training technique produces more accurate fits to both the potential-energy surface as well as the corresponding force fields than the previous methods. The fitting and interpolation accuracy is so high (rms error=1.2 cm-1) that trajectories computed on the NN potential exhibit point-by-point agreement with corresponding

  16. Methods and procedures for the verification and validation of artificial neural networks

    CERN Document Server

    Taylor, Brian J

    2006-01-01

    Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. This volume introduces some of the methods and techniques used for the verification and validation of neural networks and adaptive systems.

  17. Response to ``Comment on `Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks''' [Chaos 17, 038101 (2007)

    Science.gov (United States)

    Yu, Wenwu; Cao, Jinde

    2007-09-01

    Parameter identification of dynamical systems from time series has received increasing interest due to its wide applications in secure communication, pattern recognition, neural networks, and so on. Given the driving system, parameters can be estimated from the time series by using an adaptive control algorithm. Recently, it has been reported that for some stable systems, in which parameters are difficult to be identified [Li et al., Phys Lett. A 333, 269-270 (2004); Remark 5 in Yu and Cao, Physica A 375, 467-482 (2007); and Li et al., Chaos 17, 038101 (2007)], and in this paper, a brief discussion about whether parameters can be identified from time series is investigated. From some detailed analyses, the problem of why parameters of stable systems can be hardly estimated is discussed. Some interesting examples are drawn to verify the proposed analysis.

  18. Distributed Adaptive Neural Network Output Tracking of Leader-Following High-Order Stochastic Nonlinear Multiagent Systems With Unknown Dead-Zone Input.

    Science.gov (United States)

    Hua, Changchun; Zhang, Liuliu; Guan, Xinping

    2017-01-01

    This paper studies the problem of distributed output tracking consensus control for a class of high-order stochastic nonlinear multiagent systems with unknown nonlinear dead-zone under a directed graph topology. The adaptive neural networks are used to approximate the unknown nonlinear functions and a new inequality is used to deal with the completely unknown dead-zone input. Then, we design the controllers based on backstepping method and the dynamic surface control technique. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of semiglobally uniform ultimate boundedness and the tracking errors between the leader and the followers approach to a small residual set based on Lyapunov stability theory. Finally, two simulation examples are presented to show the effectiveness and the advantages of the proposed techniques.

  19. Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Christian Birk [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Grid Integration Group; Robinson, Matt [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering; Yasaei, Yasser [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Caudell, Thomas [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Martinez-Ramon, Manel [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Mammoli, Andrea [Univ. of New Mexico, Albuquerque, NM (United States). Dept. of Mechanical Engineering

    2016-07-01

    Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.

  20. Coastal wetland adaptation to sea level rise: Quantifying potential for landward migration and coastal squeeze

    Science.gov (United States)

    Borchert, Sinéad M.; Osland, Michael J.; Enwright, Nicholas M.; Griffith, Kereen

    2018-01-01

    Coastal wetland ecosystems are expected to migrate landwards in response to rising seas. However, due to differences in topography and coastal urbanization, estuaries vary in their ability to accommodate migration. Low‐lying urban areas can constrain migration and lead to wetland loss (i.e. coastal squeeze), especially where existing wetlands cannot keep pace with rising seas via vertical adjustments. In many estuaries, there is a pressing need to identify landward migration corridors and better quantify the potential for landward migration and coastal squeeze.We quantified and compared the area available for landward migration of tidal saline wetlands and the area where urban development is expected to prevent migration for 39 estuaries along the wetland‐rich USA Gulf of Mexico coast. We did so under three sea level rise scenarios (0.5, 1.0, and 1.5 m by 2100).Within the region, the potential for wetland migration is highest within certain estuaries in Louisiana and southern Florida (e.g. Atchafalaya/Vermilion Bays, Mermentau River, Barataria Bay, and the North and South Ten Thousand Islands estuaries).The potential for coastal squeeze is highest in estuaries containing major metropolitan areas that extend into low‐lying lands. The Charlotte Harbor, Tampa Bay, and Crystal‐Pithlachascotee estuaries (Florida) have the highest amounts of urban land expected to constrain wetland migration. Urban barriers to migration are also high in the Galveston Bay (Texas) and Atchafalaya/Vermilion Bays (Louisiana) estuaries.Synthesis and applications. Coastal wetlands provide many ecosystem services that benefit human health and well‐being, including shoreline protection and fish and wildlife habitat. As the rate of sea level rise accelerates in response to climate change, coastal wetland resources could be lost in areas that lack space for landward migration. Migration corridors are particularly important in highly urbanized estuaries where, due to low‐lying coastal

  1. Investigating the potential of a novel low-energy house concept with hybrid adaptable thermal storage

    International Nuclear Information System (INIS)

    Hoes, P.; Trcka, M.; Hensen, J.L.M.; Hoekstra Bonnema, B.

    2011-01-01

    Research highlights: → In conventional buildings thermal mass is a permanent building characteristic. → Permanent thermal mass concepts are not optimal in all operational conditions. → We propose a concept that combines the benefits of low and high thermal mass. → Building simulation shows the concept is able to reduce the energy demand with 35%. → Furthermore, the concept increases the performance robustness of the building. -- Abstract: In conventional buildings thermal mass is a permanent building characteristic depending on the building design. However, none of the permanent thermal mass concepts are optimal in all operational conditions. We propose a concept that combines the benefits of buildings with low and high thermal mass by applying hybrid adaptable thermal storage (HATS) systems and materials to a lightweight building. The HATS concept increases building performance and the robustness to changing user behavior, seasonal variations and future climate changes. Building performance simulation is used to investigate the potential of the novel concept for reducing heating energy demand and increasing thermal comfort. Simulation results of a case study in the Netherlands show that the optimal quantity of the thermal mass is sensitive to the change of seasons. This implies that the building performance will benefit from implementing HATS. Furthermore, the potential of HATS is quantified using a simplified HATS model. Calculations show heating energy demand reductions of up to 35% and increased thermal comfort compared to conventional thermal mass concepts.

  2. Promoting adaptive flood risk management: the role and potential of flood recovery mechanisms

    Directory of Open Access Journals (Sweden)

    Priest Sally J

    2016-01-01

    Full Text Available There is a high potential for recovery mechanisms to be used to incentivise the uptake of flood mitigation and loss reduction measures, undertake adaptation and promote community resilience. Indeed, creating a resilient response to flooding requires flood risk management approaches to be aligned and it needs to be ensured that recovery mechanisms to not provide disincentives for individuals and business to take proactive action to reduce risk. However, the degree to which it is desirable and effective for insurers and governments providing compensation to promote resilience and risk reduction depends upon how the cover or compensation is organised and the premiums which are charged. A review of international flood recovery mechanisms has been undertaken to identify firstly the types of schemes that exist and their characteristics. Analysis of existing instruments highlights that there are various potential approaches to encourage or require the uptake of flood mitigation and also discourage the construction of new development in high flood risk. However despite the presence of these instruments, those organising recovery mechanisms could be doing much more to incentivise increased resilience.

  3. Absence of direction-specific cross-modal visual-auditory adaptation in motion-onset event-related potentials.

    Science.gov (United States)

    Grzeschik, Ramona; Lewald, Jörg; Verhey, Jesko L; Hoffmann, Michael B; Getzmann, Stephan

    2016-01-01

    Adaptation to visual or auditory motion affects within-modality motion processing as reflected by visual or auditory free-field motion-onset evoked potentials (VEPs, AEPs). Here, a visual-auditory motion adaptation paradigm was used to investigate the effect of visual motion adaptation on VEPs and AEPs to leftward motion-onset test stimuli. Effects of visual adaptation to (i) scattered light flashes, and motion in the (ii) same or in the (iii) opposite direction of the test stimulus were compared. For the motion-onset VEPs, i.e. the intra-modal adaptation conditions, direction-specific adaptation was observed--the change-N2 (cN2) and change-P2 (cP2) amplitudes were significantly smaller after motion adaptation in the same than in the opposite direction. For the motion-onset AEPs, i.e. the cross-modal adaptation condition, there was an effect of motion history only in the change-P1 (cP1), and this effect was not direction-specific--cP1 was smaller after scatter than after motion adaptation to either direction. No effects were found for later components of motion-onset AEPs. While the VEP results provided clear evidence for the existence of a direction-specific effect of motion adaptation within the visual modality, the AEP findings suggested merely a motion-related, but not a direction-specific effect. In conclusion, the adaptation of veridical auditory motion detectors by visual motion is not reflected by the AEPs of the present study. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  4. Drought-adaptation potential in Fagus sylvatica: linking moisture availability with genetic diversity and dendrochronology.

    Directory of Open Access Journals (Sweden)

    Andrea R Pluess

    Full Text Available BACKGROUND: Microevolution is essential for species persistence especially under anticipated climate change scenarios. Species distribution projection models suggested that the dominant tree species of lowland forests in Switzerland, European beech (Fagus sylvatica L., might disappear from most areas due to expected longer dry periods. However, if genotypes at the moisture boundary of the species climatic envelope are adapted to lower moisture availability, they can serve as seed source for the continuation of beech forests under changing climates. METHODOLOGY/PRINCIPAL FINDINGS: With an AFLP genome scan approach, we studied neutral and potentially adaptive genetic variation in Fagus sylvatica in three regions containing a dry and a mesic site each (n(ind. = 241, n(markers = 517. We linked this dataset with dendrochronological growth measures and local moisture availabilities based on precipitation and soil characteristics. Genetic diversity decreased slightly at dry sites. Overall genetic differentiation was low (F(st = 0.028 and Bayesian cluster analysis grouped all populations together suggesting high (historical gene flow. The Bayesian outlier analyses indicated 13 markers with three markers differing between all dry and mesic sites and the others between the contrasting sites within individual regions. A total of 41 markers, including seven outlier loci, changed their frequency with local moisture availability. Tree height and median basal growth increments were reduced at dry sites, but marker presence/absence was not related to dendrochronological characteristics. CONCLUSION AND THEIR SIGNIFICANCE: The outlier alleles and the makers with changing frequencies in relation to moisture availability indicate microevolutionary processes occurring within short geographic distances. The general genetic similarity among sites suggests that 'preadaptive' genes can easily spread across the landscape. Yet, due to the long live span of

  5. Drought-adaptation potential in Fagus sylvatica: linking moisture availability with genetic diversity and dendrochronology.

    Science.gov (United States)

    Pluess, Andrea R; Weber, Pascale

    2012-01-01

    Microevolution is essential for species persistence especially under anticipated climate change scenarios. Species distribution projection models suggested that the dominant tree species of lowland forests in Switzerland, European beech (Fagus sylvatica L.), might disappear from most areas due to expected longer dry periods. However, if genotypes at the moisture boundary of the species climatic envelope are adapted to lower moisture availability, they can serve as seed source for the continuation of beech forests under changing climates. With an AFLP genome scan approach, we studied neutral and potentially adaptive genetic variation in Fagus sylvatica in three regions containing a dry and a mesic site each (n(ind.) = 241, n(markers) = 517). We linked this dataset with dendrochronological growth measures and local moisture availabilities based on precipitation and soil characteristics. Genetic diversity decreased slightly at dry sites. Overall genetic differentiation was low (F(st) = 0.028) and Bayesian cluster analysis grouped all populations together suggesting high (historical) gene flow. The Bayesian outlier analyses indicated 13 markers with three markers differing between all dry and mesic sites and the others between the contrasting sites within individual regions. A total of 41 markers, including seven outlier loci, changed their frequency with local moisture availability. Tree height and median basal growth increments were reduced at dry sites, but marker presence/absence was not related to dendrochronological characteristics. CONCLUSION AND THEIR SIGNIFICANCE: The outlier alleles and the makers with changing frequencies in relation to moisture availability indicate microevolutionary processes occurring within short geographic distances. The general genetic similarity among sites suggests that 'preadaptive' genes can easily spread across the landscape. Yet, due to the long live span of trees, fostering saplings originating from dry sites and

  6. Potential impacts of climate change and adaptation strategies for sunflower in Pakistan.

    Science.gov (United States)

    Awais, Muhammad; Wajid, Aftab; Saleem, Muhammad Farrukh; Nasim, Wajid; Ahmad, Ashfaq; Raza, Muhammad Aown Sammar; Bashir, Muhammad Usman; Mubeen, Muhammad; Hammad, Hafiz Mohkum; Habib Ur Rahman, Muhammad; Saeed, Umer; Arshad, Muhammad Naveed; Hussain, Jamshad

    2018-05-01

    Growth, development, and economic yield of agricultural crops rely on moisture, temperature, light, and carbon dioxide concentration. However, the amount of these parameters is varying with time due to climate change. Climate change is factual and ongoing so, first principle of agronomy should be to identify climate change potential impacts and adaptation measures to manage the susceptibilities of agricultural sector. Crop models have ability to predict the crop's yield under changing climatic conditions. We used OILCROP-SUN model to simulate the influence of elevated temperature and CO 2 on crop growth duration, maximum leaf area index (LAI), total dry matter (TDM), and achene yield of sunflower under semi-arid conditions of Pakistan (Faisalabad, Punjab). The model was calibrated and validated with the experimental data of 2012 and 2013, respectively. The simulation results showed that phenological events of sunflower were not changed at higher concentration of CO 2 (430 and 550 ppm). However LAI, achene yield, and TDM increased by 0.24, 2.41, and 4.67% at 430 ppm and by 0.48, 3.09, and 9.87% at 550 ppm, respectively. Increased temperature (1 and 2 °C) reduced the sunflower duration to remain green that finally led to less LAI, achene yield, and TDM as compared to present conditions. However, the drastic effects of increased temperature on sunflower were reduced to some extent at 550 ppm CO 2 concentration. Evaluation of different adaptation options revealed that 21 days earlier (as compared to current sowing date) planting of sunflower crop with increased plant population (83,333 plants ha -1 ) could reduce the yield losses due to climate change. Flowering is the most critical stage of sunflower to water scarcity. We recommended skipping second irrigation or 10% (337.5 mm) less irrigation water application to conserve moisture under possible water scarce conditions of 2025 and 2050.

  7. Assessing bio-economic impacts and climate adaptation potential in Flanders

    Science.gov (United States)

    Gobin, A.

    2009-04-01

    . The interaction between these effects depends on the crop type. The impact on crop production was simulated with a dynamic vegetation model for eight crops (winter wheat, potatoes, sugar beet, fodder maize, grass, grain maize, cauliflower spring, cauliflower autumn), three soil types (loamy sand, loam, clay) and four climatic data series (historic and three cc-scenarios). The three climate change scenarios were selected on the basis of multi-criteria analysis of the PRUDENCE RCM runs. In total 3480 year simulations were executed with a daily modelling step. Pronounced yield losses mainly due to water shortages and heat stress occur for all climate change scenario's, to a lesser extent in the case of winter and spring crops. Yield losses of up to 30% are simulated for sugar beet, whereas winter wheat losses are only 6 % on loamy sand. High critical temperatures lead to heat stress, decreased fodder uptake, outbreaks of diseases and ultimately to animal production losses. Changes in animal production were calculated with a threshold model, whereby a daily maximum temperature of 30°C was taken as the production limit. Calculated animal production losses are up to 9 % for sheep, 8 % for cattle, 6 % for pigs and 3% for poultry. An economic prognosis of the technical productivity, the price effect, the required agricultural area and number of animals was used to estimate the potential productivity for 16 agricultural activities. The impact of climate change was included through aggregating the modelled production losses for Flanders and assuming the agricultural area, the number of animals and the prices constant to the economic prognosis. The total financial impacts are 0.1 % or 6.6 million euro for the first scenario, 1.5% or 71 million euro for the second scenario and 4.1% or 201 million euro for the third scenario. The results represent the acceptable cost of adaptation measures to maintain current efficiencies and production levels. Three gradations of adaptation were

  8. Neurofeedback of slow cortical potentials: neural mechanisms and feasibility of a placebo-controlled design in healthy adults

    Directory of Open Access Journals (Sweden)

    Holger eGevensleben

    2014-12-01

    Full Text Available To elucidate basic mechanisms underlying neurofeedback we investigated neural mechanisms of training of slow cortical potentials by considering EEG- and fMRI. Additionally, we analyzed the feasibility of a double-blind, placebo-controlled design in NF research based on regulation performance during treatment sessions and self-assessment of the participants. Twenty healthy adults participated in 16 sessions of SCP training: 9 participants received regular SCP training, 11 participants received sham feedback. At three time points (pre, intermediate, post fMRI and EEG/ERP-measurements were conducted during a continuous performance test (CPT. Performance-data during the sessions (regulation performance in the treatment group and the placebo group were analyzed. Analysis of EEG-activity revealed in the SCP group a strong enhancement of the CNV (electrode Cz at the intermediate assessment, followed by a decrease back to baseline at the post-treatment assessment. In contrast, in the placebo group a continuous but smaller increase of the CNV could be obtained from pre to post assessment. The increase of the CNV in the SCP group at intermediate testing was superior to the enhancement in the placebo group. The changes of the CNV were accompanied by a continuous improvement in the test performance of the CPT from pre to intermediate to post assessment comparable in both groups. The change of the CNV in the SCP group is interpreted as an indicator of neural plasticity and efficiency while an increase of the CNV in the placebo group might reflect learning and improved timing due to the frequent task repetition.In the fMRI analysis evidence was obtained for neuronal plasticity. After regular SCP neurofeedback activation in the posterior parietal cortex decreased from the pre- to the intermediate measurement and increased again in the post measurement, inversely following the U-shaped increase and decrease of the tCNV EEG amplitude in the SCP-trained group

  9. Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

    Science.gov (United States)

    Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

    2018-04-01

    This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

  10. Human cytomegalovirus IE1 downregulates Hes1 in neural progenitor cells as a potential E3 ubiquitin ligase.

    Directory of Open Access Journals (Sweden)

    Xi-Juan Liu

    2017-07-01

    Full Text Available Congenital human cytomegalovirus (HCMV infection is the leading cause of neurological disabilities in children worldwide, but the mechanisms underlying these disorders are far from well-defined. HCMV infection has been shown to dysregulate the Notch signaling pathway in human neural progenitor cells (NPCs. As an important downstream effector of Notch signaling, the transcriptional regulator Hairy and Enhancer of Split 1 (Hes1 is essential for governing NPC fate and fetal brain development. In the present study, we report that HCMV infection downregulates Hes1 protein levels in infected NPCs. The HCMV 72-kDa immediate-early 1 protein (IE1 is involved in Hes1 degradation by assembling a ubiquitination complex and promoting Hes1 ubiquitination as a potential E3 ubiquitin ligase, followed by proteasomal degradation of Hes1. Sp100A, an important component of PML nuclear bodies, is identified to be another target of IE1-mediated ubiquitination. A C-terminal acidic region in IE1, spanning amino acids 451 to 475, is required for IE1/Hes1 physical interaction and IE1-mediated Hes1 ubiquitination, but is dispensable for IE1/Sp100A interaction and ubiquitination. Our study suggests a novel mechanism linking downregulation of Hes1 protein to neurodevelopmental disorders caused by HCMV infection. Our findings also complement the current knowledge of herpesviruses by identifying IE1 as the first potential HCMV-encoded E3 ubiquitin ligase.

  11. Exploring the potential of neurophysiological measures for user-adaptive visualization

    OpenAIRE

    Tak, S.; Brouwer, A.M.; Toet, A.; Erp, J.B.F. van

    2013-01-01

    User-adaptive visualization aims to adapt visualized information to the needs and characteristics of the individual user. Current approaches deploy user personality factors, user behavior and preferences, and visual scanning behavior to achieve this goal. We argue that neurophysiological data provide valuable additional input for user-adaptive visualization systems since they contain a wealth of objective information about user characteristics. The combination of neurophysiological data with ...

  12. Sensitive Tumorigenic Potential Evaluation of Adult Human Multipotent Neural Cells Immortalized by hTERT Gene Transduction.

    Directory of Open Access Journals (Sweden)

    Kee Hang Lee

    Full Text Available Stem cells and therapeutic genes are emerging as a new therapeutic approach to treat various neurodegenerative diseases with few effective treatment options. However, potential formation of tumors by stem cells has hampered their clinical application. Moreover, adequate preclinical platforms to precisely test tumorigenic potential of stem cells are controversial. In this study, we compared the sensitivity of various animal models for in vivo stem cell tumorigenicity testing to identify the most sensitive platform. Then, tumorigenic potential of adult human multipotent neural cells (ahMNCs immortalized by the human telomerase reverse transcriptase (hTERT gene was examined as a stem cell model with therapeutic genes. When human glioblastoma (GBM cells were injected into adult (4-6-week-old Balb/c-nu, adult NOD/SCID, adult NOG, or neonate (1-2-week-old NOG mice, the neonate NOG mice showed significantly faster tumorigenesis than that of the other groups regardless of intracranial or subcutaneous injection route. Two kinds of ahMNCs (682TL and 779TL were primary cultured from surgical samples of patients with temporal lobe epilepsy. Although the ahMNCs were immortalized by lentiviral hTERT gene delivery (hTERT-682TL and hTERT-779TL, they did not form any detectable masses, even in the most sensitive neonate NOG mouse platform. Moreover, the hTERT-ahMNCs had no gross chromosomal abnormalities on a karyotype analysis. Taken together, our data suggest that neonate NOG mice could be a sensitive animal platform to test tumorigenic potential of stem cell therapeutics and that ahMNCs could be a genetically stable stem cell source with little tumorigenic activity to develop regenerative treatments for neurodegenerative diseases.

  13. Normative findings of electrically evoked compound action potential measurements using the neural response telemetry of the Nucleus CI24M cochlear implant system.

    NARCIS (Netherlands)

    Cafarelli-Dees, D.; Dillier, N.; Lai, W.K.; Wallenberg, E. von; Dijk, B. van; Akdas, F.; Aksit, M.; Batman, C.; Beynon, A.J.; Burdo, S.; Chanal, J.M.; Collet, L.; Conway, M.; Coudert, C.; Craddock, L.; Cullington, H.; Deggouj, N.; Fraysse, B.; Grabel, S.; Kiefer, J.; Kiss, J.G.; Lenarz, T.; Mair, A.; Maune, S.; Muller-Deile, J.; Piron, J.P.; Razza, S.; Tasche, C.; Thai-Van, H.; Toth, F.; Truy, E.; Uziel, A.; Smoorenburg, G.F.

    2005-01-01

    One hundred and forty-seven adult recipients of the Nucleus 24 cochlear implant system, from 13 different European countries, were tested using neural response telemetry to measure the electrically evoked compound action potential (ECAP), according to a standardised postoperative measurement

  14. Potentiating mGluR5 Function with a Positive Allosteric Modulator Enhances Adaptive Learning

    Science.gov (United States)

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J.; Nomura, Toshihiro; Stauffer, Shaun R.; Lindsley, Craig W.; Conn, P. Jeffrey; Contractor, Anis

    2013-01-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5…

  15. Adaptive Pathways: Possible Next Steps for Payers in Preparation for Their Potential Implementation

    Science.gov (United States)

    Vella Bonanno, Patricia; Ermisch, Michael; Godman, Brian; Martin, Antony P.; Van Den Bergh, Jesper; Bezmelnitsyna, Liudmila; Bucsics, Anna; Arickx, Francis; Bybau, Alexander; Bochenek, Tomasz; van de Casteele, Marc; Diogene, Eduardo; Eriksson, Irene; Fürst, Jurij; Gad, Mohamed; Greičiūtė-Kuprijanov, Ieva; van der Graaff, Martin; Gulbinovic, Jolanta; Jones, Jan; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Langner, Irene; Mardare, Ileana; Markovic-Pekovic, Vanda; Magnusson, Einar; Melien, Oyvind; Meshkov, Dmitry O.; Petrova, Guenka I.; Selke, Gisbert; Sermet, Catherine; Simoens, Steven; Schuurman, Ad; Ramos, Ricardo; Rodrigues, Jorge; Zara, Corinne; Zebedin-Brandl, Eva; Haycox, Alan

    2017-01-01

    Medicines receiving a conditional marketing authorization through Medicines Adaptive Pathways to Patients (MAPPs) will be a challenge for payers. The “introduction” of MAPPs is already seen by the European Medicines Agency (EMA) as a fait accompli, with payers not consulted or involved. However, once medicines are approved through MAPPs, they will be evaluated for funding by payers through different activities. These include Health Technology Assessment (HTA) with often immature clinical data and high uncertainty, financial considerations, and negotiations through different types of agreements, which can require monitoring post launch. Payers have experience with new medicines approved through conditional approval, and the fact that MAPPs present additional challenges is a concern from their perspective. There may be some activities where payers can collaborate. The final decisions on whether to reimburse a new medicine via MAPPs will have more variation than for medicines licensed via conventional processes. This is due not only to increasing uncertainty associated with medicines authorized through MAPPs but also differences in legal frameworks between member states. Moreover, if the financial and side-effect burden from the period of conditional approval until granting full marketing authorization is shifted to the post-authorization phase, payers may have to bear such burdens. Collection of robust data during routine clinical use is challenging along with high prices for new medicines during data collection. This paper presents the concept of MAPPs and possible challenges. Concerns and potential ways forward are discussed and a number of recommendations are presented from the perspective of payers. PMID:28878667

  16. Climate Change Potential Impacts on the Built Environment and Possible Adaptation Strategies

    Science.gov (United States)

    Quattrochi, Dale A.

    2014-01-01

    The built environment consists of components that exist at a range of scales from small (e.g., houses, shopping malls) to large (e.g., transportation networks) to highly modified landscapes such as cities. Thus, the impacts of climate change on the built environment may have a multitude of effects on humans and the land. The impact of climate change may be exacerbated by the interaction of different events that singly may be minor, but together may have a synergistic set of impacts that are significant. Also, mechanisms may exist wherein the built environment, particularly in the form of cities, may affect weather and the climate on local and regional scales. Hence, a city may be able to cope with prolonged heat waves, but if this is combined with severe drought, the overall result could be significant or even catastrophic, as accelerating demand for energy to cooling taxes water supplies needed both for energy supply and municipal water needs. This presentation surveys potential climate change impacts on the built environment from the perspective of the National Climate Assessment, and explores adaptation measures that can be employed to mitigate these impacts.

  17. Adaptive Pathways: Possible Next Steps for Payers in Preparation for Their Potential Implementation.

    Science.gov (United States)

    Vella Bonanno, Patricia; Ermisch, Michael; Godman, Brian; Martin, Antony P; Van Den Bergh, Jesper; Bezmelnitsyna, Liudmila; Bucsics, Anna; Arickx, Francis; Bybau, Alexander; Bochenek, Tomasz; van de Casteele, Marc; Diogene, Eduardo; Eriksson, Irene; Fürst, Jurij; Gad, Mohamed; Greičiūtė-Kuprijanov, Ieva; van der Graaff, Martin; Gulbinovic, Jolanta; Jones, Jan; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Langner, Irene; Mardare, Ileana; Markovic-Pekovic, Vanda; Magnusson, Einar; Melien, Oyvind; Meshkov, Dmitry O; Petrova, Guenka I; Selke, Gisbert; Sermet, Catherine; Simoens, Steven; Schuurman, Ad; Ramos, Ricardo; Rodrigues, Jorge; Zara, Corinne; Zebedin-Brandl, Eva; Haycox, Alan

    2017-01-01

    Medicines receiving a conditional marketing authorization through Medicines Adaptive Pathways to Patients (MAPPs) will be a challenge for payers. The "introduction" of MAPPs is already seen by the European Medicines Agency (EMA) as a fait accompli, with payers not consulted or involved. However, once medicines are approved through MAPPs, they will be evaluated for funding by payers through different activities. These include Health Technology Assessment (HTA) with often immature clinical data and high uncertainty, financial considerations, and negotiations through different types of agreements, which can require monitoring post launch. Payers have experience with new medicines approved through conditional approval, and the fact that MAPPs present additional challenges is a concern from their perspective. There may be some activities where payers can collaborate. The final decisions on whether to reimburse a new medicine via MAPPs will have more variation than for medicines licensed via conventional processes. This is due not only to increasing uncertainty associated with medicines authorized through MAPPs but also differences in legal frameworks between member states. Moreover, if the financial and side-effect burden from the period of conditional approval until granting full marketing authorization is shifted to the post-authorization phase, payers may have to bear such burdens. Collection of robust data during routine clinical use is challenging along with high prices for new medicines during data collection. This paper presents the concept of MAPPs and possible challenges. Concerns and potential ways forward are discussed and a number of recommendations are presented from the perspective of payers.

  18. Human neural tuning estimated from compound action potentials in normal hearing human volunteers

    Science.gov (United States)

    Verschooten, Eric; Desloovere, Christian; Joris, Philip X.

    2015-12-01

    The sharpness of cochlear frequency tuning in humans is debated. Evoked otoacoustic emissions and psychophysical measurements suggest sharper tuning in humans than in laboratory animals [15], but this is disputed based on comparisons of behavioral and electrophysiological measurements across species [14]. Here we used evoked mass potentials to electrophysiologically quantify tuning (Q10) in humans. We combined a notched noise forward masking paradigm [9] with the recording of trans tympanic compound action potentials (CAP) from masked probe tones in awake human and anesthetized monkey (Macaca mulatta). We compare our results to data obtained with the same paradigm in cat and chinchilla [16], and find that CAP-Q10values in human are ˜1.6x higher than in cat and chinchilla and ˜1.3x higher than in monkey. To estimate frequency tuning of single auditory nerve fibers (ANFs) in humans, we derive conversion functions from ANFs in cat, chinchilla, and monkey and apply these to the human CAP measurements. The data suggest that sharp cochlear tuning is a feature of old-world primates.

  19. Neural Temporal Dynamics of Social Exclusion Elicited by Averted Gaze: An Event-Related Potentials Study

    Directory of Open Access Journals (Sweden)

    Yue Leng

    2018-02-01

    Full Text Available Eye gaze plays a fundamental role in social communication. The averted eye gaze during social interaction, as the most common form of silent treatment, conveys a signal of social exclusion. In the present study, we examined the time course of brain response to social exclusion by using a modified version of Eye-gaze paradigm. The event-related potentials (ERPs data and the subjective rating data showed that the frontocentral P200 was positively correlated with negative mood of excluded events, whereas, the centroparietal late positive potential (LPP was positively correlated with the perceived ostracism intensity. Both the P200 and LPP were more positive-going for excluded events than for included events. These findings suggest that brain responses sensitive to social exclusion can be divided into the early affective processing stage, linking to the early pre-cognitive warning system; and the late higher-order processes stage, demanding attentional resources for elaborate stimuli evaluation and categorization generally not under specific situation.

  20. Lifelong bilingualism and neural reserve against Alzheimer's disease: a review of findings and potential mechanisms.

    Science.gov (United States)

    Gold, Brian T

    2015-03-15

    Alzheimer's disease (AD) is a progressive brain disorder that initially affects medial temporal lobe circuitry and memory functions. Current drug treatments have only modest effects on the symptomatic course of the disease. In contrast, a growing body of evidence suggests that lifelong bilingualism may delay the onset of clinical AD symptoms by several years. The purpose of the present review is to summarize evidence for bilingualism as a reserve variable against AD and discuss potential underlying neurocognitive mechanisms. Evidence is reviewed suggesting that bilingualism may delay clinical AD symptoms by protecting frontostriatal and frontoparietal executive control circuitry rather than medial temporal lobe memory circuitry. Cellular and molecular mechanisms that may contribute to bilingual cognitive reserve effects are discussed, including those that may affect neuronal metabolic functions, dynamic neuronal-glial interactions, vascular factors, myelin structure and neurochemical signaling. Future studies that may test some of these potential mechanisms of bilingual CR effects are proposed. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Effects of loss aversion on neural responses to loss outcomes: An event-related potential study.

    Science.gov (United States)

    Kokmotou, Katerina; Cook, Stephanie; Xie, Yuxin; Wright, Hazel; Soto, Vicente; Fallon, Nicholas; Giesbrecht, Timo; Pantelous, Athanasios; Stancak, Andrej

    2017-05-01

    Loss aversion is the tendency to prefer avoiding losses over acquiring gains of the same amount. To shed light on the spatio-temporal processes underlying loss aversion, we analysed the associations between individual loss aversion and electrophysiological responses to loss and gain outcomes in a monetary gamble task. Electroencephalographic feedback-related negativity (FRN) was computed in 29 healthy participants as the difference in electrical potentials between losses and gains. Loss aversion was evaluated using non-linear parametric fitting of choices in a separate gamble task. Loss aversion correlated positively with FRN amplitude (233-263ms) at electrodes covering the lower face. Feedback related potentials were modelled by five equivalent source dipoles. From these dipoles, stronger activity in a source located in the orbitofrontal cortex was associated with loss aversion. The results suggest that loss aversion implemented during risky decision making is related to a valuation process in the orbitofrontal cortex, which manifests during learning choice outcomes. Copyright © 2017. Published by Elsevier B.V.

  2. Impact of glacier shrinkage and adapted hydropower potential in the Swiss Alps

    International Nuclear Information System (INIS)

    Terrier, Stephane; Bieri, Martin; Jordan, Frederic; Schleiss, Anton J.

    2015-01-01

    Global warming is an alarming reality and likely leads to an increase of multiple pressures on socio-economic systems. However, in high-mountain regions it might also become an opportunity to adapt existing hydropower schemes and to develop new projects to this reality. In the Alps, the melting of glaciers first produces over the near future an increase of the average annual discharge depending on glacier and catchment characteristics, especially during the summer season. Nevertheless after a certain time, significant decrease of runoff related to glacier melting must be considered for hydropower management. Moreover, the shrinking glaciers free new areas, having the potential for the construction of new dams and reservoirs. The opportunity to build new dams and hydropower plants downstream of retreating glaciers is studied using two models. The first (GlabTop) is used to predict the future topography and geomorphology underneath the melting glaciers, in order to define the optimal locations of the future dams and reservoirs. Secondly, the RS3.0 CLIMATE rainfall-runoff hydrological model computes the glacier evolution, the river discharge at the outlet of the catchment area as well as the hydropower production of the new schemes. As a case study the Upper Aare River basin in Switzerland is presented. The opportunity of the construction of a new dam and a hydropower plant is studied, including its economic benefit. The result of the case study provides a basis to assess the potential of investing in such projects to ensure the Swiss hydroelectricity production also in future as well as peak energy for the European grid. (authors)

  3. Neural correlates of economic value and valuation context: an event-related potential study.

    Science.gov (United States)

    Tyson-Carr, John; Kokmotou, Katerina; Soto, Vicente; Cook, Stephanie; Fallon, Nicholas; Giesbrecht, Timo; Stancak, Andrej

    2018-05-01

    The value of environmental cues and internal states is continuously evaluated by the human brain, and it is this subjective value that largely guides decision making. The present study aimed to investigate the initial value attribution process, specifically the spatiotemporal activation patterns associated with values and valuation context, using electroencephalographic event-related potentials (ERPs). Participants completed a stimulus rating task in which everyday household items marketed up to a price of £4 were evaluated with respect to their desirability or material properties. The subjective values of items were evaluated as willingness to pay (WTP) in a Becker-DeGroot-Marschak auction. On the basis of the individual's subjective WTP values, the stimuli were divided into high- and low-value items. Source dipole modeling was applied to estimate the cortical sources underlying ERP components modulated by subjective values (high vs. low WTP) and the evaluation condition (value-relevant vs. value-irrelevant judgments). Low-WTP items and value-relevant judgments both led to a more pronounced N2 visual evoked potential at right frontal scalp electrodes. Source activity in right anterior insula and left orbitofrontal cortex was larger for low vs. high WTP at ∼200 ms. At a similar latency, source activity in right anterior insula and right parahippocampal gyrus was larger for value-relevant vs. value-irrelevant judgments. A stronger response for low- than high-value items in anterior insula and orbitofrontal cortex appears to reflect aversion to low-valued item acquisition, which in an auction experiment would be perceived as a relative loss. This initial low-value bias occurs automatically irrespective of the valuation context. NEW & NOTEWORTHY We demonstrate the spatiotemporal characteristics of the brain valuation process using event-related potentials and willingness to pay as a measure of subjective value. The N2 component resolves values of objects with a

  4. Neural correlates of cued recall in young and older adults: an event-related potential study.

    Science.gov (United States)

    Angel, Lucie; Fay, Séverine; Bouazzaoui, Badiâa; Granjon, Lionel; Isingrini, Michel

    2009-01-07

    This experiment investigated age differences in electrophysiological correlates of retrieval success in a word-stem cued recall task. Young adults (M+/-SD: 21.4 years+/-1.9) performed this memory task more accurately than older participants (M+/-SD: 65.1 years+/-3.3). Robust event-related brain potential (ERP) old/new effects were identified in both age groups. The main age differences were observed in latency and lateralization of ERP effects. Young adults exhibited a parietal effect that became focused over left parietal electrodes, whereas no asymmetry was observed in older adults. Moreover, ERP effects were more delayed in the older group. Overall, these findings provide some evidence of the reduction of processing speed during aging and suggest that young and older adults may recruit distinct cerebral patterns during episodic cued recall.

  5. When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

    Science.gov (United States)

    Shi, Yiquan; Wolfensteller, Uta; Schubert, Torsten; Ruge, Hannes

    2018-02-01

    Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching). © 2017 Wiley Periodicals, Inc.

  6. Exploring the potential of neurophysiological measures for user-adaptive visualization

    NARCIS (Netherlands)

    Tak, S.; Brouwer, A.M.; Toet, A.; Erp, J.B.F. van

    2013-01-01

    User-adaptive visualization aims to adapt visualized information to the needs and characteristics of the individual user. Current approaches deploy user personality factors, user behavior and preferences, and visual scanning behavior to achieve this goal. We argue that neurophysiological data

  7. Big data has big potential for applications to climate change adaptation

    NARCIS (Netherlands)

    Ford, James D.; Tilleard, Simon E.; Berrang-Ford, Lea; Araos, Malcolm; Biesbroek, Robbert; Lesnikowski, Alexandra C.; MacDonald, Graham K.; Hsu, Angel; Chen, Chen; Bizikova, Livia

    2016-01-01

    The capacity to collect and analyze massive amounts
    of data is transforming research in the natural and social
    sciences (1). And yet, the climate change adaptation
    community has largely overlooked these developments.
    Here, we examine how “big data” can inform adaptation
    research

  8. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

    Science.gov (United States)

    Bandeira Diniz, João Otávio; Bandeira Diniz, Pedro Henrique; Azevedo Valente, Thales Levi; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso; Gattass, Marcelo

    2018-03-01

    The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for

  9. Altered Neural Activity during Semantic Object Memory Retrieval in Amnestic Mild Cognitive Impairment as Measured by Event-Related Potentials.

    Science.gov (United States)

    Chiang, Hsueh-Sheng; Mudar, Raksha A; Pudhiyidath, Athula; Spence, Jeffrey S; Womack, Kyle B; Cullum, C Munro; Tanner, Jeremy A; Eroh, Justin; Kraut, Michael A; Hart, John

    2015-01-01

    Deficits in semantic memory in individuals with amnestic mild cognitive impairment (aMCI) have been previously reported, but the underlying neurobiological mechanisms remain to be clarified. We examined event-related potentials (ERPs) associated with semantic memory retrieval in 16 individuals with aMCI as compared to 17 normal controls using the Semantic Object Retrieval Task (EEG SORT). In this task, subjects judged whether pairs of words (object features) elicited retrieval of an object (retrieval trials) or not (non-retrieval trials). Behavioral findings revealed that aMCI subjects had lower accuracy scores and marginally longer reaction time compared to controls. We used a multivariate analytical technique (STAT-PCA) to investigate similarities and differences in ERPs between aMCI and control groups. STAT-PCA revealed a left fronto-temporal component starting at around 750 ms post-stimulus in both groups. However, unlike controls, aMCI subjects showed an increase in the frontal-parietal scalp potential that distinguished retrieval from non-retrieval trials between 950 and 1050 ms post-stimulus negatively correlated with the performance on the logical memory subtest of the Wechsler Memory Scale-III. Thus, individuals with aMCI were not only impaired in their behavioral performance on SORT relative to controls, but also displayed alteration in the corresponding ERPs. The altered neural activity in aMCI compared to controls suggests a more sustained and effortful search during object memory retrieval, which may be a potential marker indicating disease processes at the pre-dementia stage.

  10. The neural basis of analogical reasoning: an event-related potential study.

    Science.gov (United States)

    Qiu, Jiang; Li, Hong; Chen, Antao; Zhang, Qinglin

    2008-10-01

    The spatiotemporal analysis of brain activation during the execution of easy analogy (EA) and difficult analogy (DA) tasks was investigated using high-density event-related brain potentials (ERPs). Results showed that reasoning tasks (schema induction) elicited a more negative ERP deflection (N500-1000) than did the baseline task (BS) between 500 and 1000 ms. Dipole source analysis of difference waves (EA-BS and DA-BS) indicated that the negative components were both localized near the left thalamus, possibly associated with the retrieval of alphabetical information. Furthermore, DA elicited a more positive ERP component (P600-1000) than did EA in the same time window. Two generators of P600-1000 were located in the medial prefrontal cortex (BA10) and the left frontal cortex (BA6) which was possibly involved in integrating information in schema abstraction. In the stage of analogy mapping, a greater negativity (N400-600) in the reasoning tasks as compared to BS was found over fronto-central scalp regions. A generator of this effect was located in the left fusiform gyrus and was possibly related to associative memory and activation of schema. Then, a greater negativity in the reasoning tasks, in comparison to BS task, developed between 900-1200 ms (LNC1) and 2000-2500 ms (LNC2). Dipole source analysis (EA-BS) localized the generator of LNC1 in the left prefrontal cortex (BA 10) which was possibly related to mapping the schema to the target problem, and the generator of LNC2 in the left prefrontal cortex (BA 9) which was possibly related to deciding whether a conclusion correctly follows from the schema.

  11. Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

    Science.gov (United States)

    Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

    2013-04-01

    Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

  12. Social contexts modulate neural responses in the processing of others' pain: An event-related potential study.

    Science.gov (United States)

    Cui, Fang; Zhu, Xiangru; Luo, Yuejia

    2017-08-01

    Two hypotheses have been proposed regarding the response that is triggered by observing others' pain: the "empathizing hypothesis" and the "threat value of pain hypothesis." The former suggests that observing others' pain triggers an empathic response. The latter suggests that it activates the threat-detection system. In the present study, participants were instructed to observe pictures that showed an anonymous hand or foot in a painful or non-painful situation in a threatening or friendly social context. Event-related potentials were recorded when the participants passively observed these pictures in different contexts. We observed an interaction between context and picture in the early automatic N1 component, in which the painful pictures elicited a larger amplitude than the non-painful pictures only in the threatening context and not in the friendly context. We also observed an interaction between context and picture in the late P3 component, in which the painful pictures elicited a larger amplitude than the non-painful pictures only in the friendly context and not in the threatening context. These results indicate that specific social contexts can modulate the neural responses to observing others' pain. The "empathic hypothesis" and "threat value of pain hypothesis" are not mutually exclusive and do not contradict each other but rather work in different temporal stages.

  13. Neural correlates of self-appraisals in the near and distant future: an event-related potential study.

    Directory of Open Access Journals (Sweden)

    Yangmei Luo

    Full Text Available To investigate perceptual and neural correlates of future self-appraisals as a function of temporal distance, event-related potentials (ERPs were recorded while participants (11 women, eight men made judgments about the applicability of trait adjectives to their near future selves (i.e., one month from now and their distant future selves (i.e., three years from now. Behavioral results indicated people used fewer positive adjectives, more negative adjectives, recalled more specific events coming to mind and felt more psychologically connected to the near future self than the distant future self. Electrophysiological results demonstrated that negative trait adjectives elicited more positive ERP deflections than did positive trait adjectives in the interval between 550 and 800 ms (late positive component within the near future self condition. However, within the same interval, there were no significant differences between negative and positive traits adjectives in the distant future self condition. The results suggest that negative emotional processing in future self-appraisals is modulated by temporal distance, consistent with predictions of construal level theory.

  14. Potential benefits of early vigor and changes in phenology in wheat to adapt to warmer and drier climates

    NARCIS (Netherlands)

    Ludwig, F.; Asseng, S.

    2010-01-01

    Developing crop cultivars with novel traits could help agriculture adapt to climate change. As introducing new traits into crops is expensive and time consuming, it is helpful to develop methods which can test whether a potential new plant trait increases or maintains production in future climates.

  15. Auto-adaptive averaging: Detecting artifacts in event-related potential data using a fully automated procedure

    NARCIS (Netherlands)

    Talsma, D.

    2008-01-01

    The auto-adaptive averaging procedure proposed here classifies artifacts in event-related potential data by optimizing the signal-to-noise ratio. This method rank orders single trials according to the impact of each trial on the ERP average. Then, the minimum residual background noise level in the

  16. Auto-adaptive averaging: Detecting artifacts in event-related potential data using a fully automated procedure.

    NARCIS (Netherlands)

    Talsma, D.

    2008-01-01

    The auto-adaptive averaging procedure proposed here classifies artifacts in event-related potential data by optimizing the signal-to-noise ratio. This method rank orders single trials according to the impact of each trial on the ERP average. Then, the minimum residual background noise level in the

  17. A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

    Science.gov (United States)

    Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian

    2018-06-01

    Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.

  18. Adaptive Pathways: Possible Next Steps for Payers in Preparation for Their Potential Implementation

    Directory of Open Access Journals (Sweden)

    Patricia Vella Bonanno

    2017-08-01

    Full Text Available Medicines receiving a conditional marketing authorization through Medicines Adaptive Pathways to Patients (MAPPs will be a challenge for payers. The “introduction” of MAPPs is already seen by the European Medicines Agency (EMA as a fait accompli, with payers not consulted or involved. However, once medicines are approved through MAPPs, they will be evaluated for funding by payers through different activities. These include Health Technology Assessment (HTA with often immature clinical data and high uncertainty, financial considerations, and negotiations through different types of agreements, which can require monitoring post launch. Payers have experience with new medicines approved through conditional approval, and the fact that MAPPs present additional challenges is a concern from their perspective. There may be some activities where payers can collaborate. The final decisions on whether to reimburse a new medicine via MAPPs will have more variation than for medicines licensed via conventional processes. This is due not only to increasing uncertainty associated with medicines authorized through MAPPs but also differences in legal frameworks between member states. Moreover, if the financial and side-effect burden from the period of conditional approval until granting full marketing authorization is shifted to the post-authorization phase, payers may have to bear such burdens. Collection of robust data during routine clinical use is challenging along with high prices for new medicines during data collection. This paper presents the concept of MAPPs and possible challenges. Concerns and potential ways forward are discussed and a number of recommendations are presented from the perspective of payers.

  19. Life history, population viability, and the potential for local adaptation in isolated trout populations

    Directory of Open Access Journals (Sweden)

    K.J. Carim

    2017-04-01

    Full Text Available Habitat loss and fragmentation have caused population decline across taxa through impacts on life history diversity, dispersal patterns, and gene flow. Yet, intentional isolation of native fish populations is a frequently used management strategy to protect against negative interactions with invasive fish species. We evaluated the population viability and genetic diversity of 12 isolated populations of Oncorhynchus clarkii lewisi located on the Flathead Indian Reservation in Montana, USA. Length-structured integral projection models (IPMs were used to project population growth rate (lambda and its sensitivity to underlying vital rates and parameters. We examined relationships between lambda, genetic diversity, and habitat size and quality. Lambda ranged from 0.68 to 1.1 with 10 of 12 populations projected to be in decline. A sensitivity analysis of lambda with respect to projection matrix elements indicated that lambda was generally sensitive to changes in early life history stages (survival/growth, but patterns differed among populations. Another sensitivity analysis with respect to underlying model parameters showed highly consistent pattern across populations, with lambda being most sensitive to the slope of probability of maturity (estimated from published literature, generally followed by adult survival, and the slope of somatic growth rate (directly measured from each population. Lambda was not correlated with genetic diversity. For populations residing in small isolated streams (≤5 km of occupied habitat, lambda significantly increased with base flow discharge (r2=0.50, p<0.02. Our results highlight the potential importance of local adaptation for persistence of small, isolated populations. Specifically we saw evidence for higher probability of maturity at smaller sizes in the smallest, coldest isolated systems, increasing probability of persistence for these populations. Climate change threatens to further fragment populations of

  20. Acute inhibition of estradiol synthesis impacts vestibulo-ocular reflex adaptation and cerebellar long-term potentiation in male rats.

    Science.gov (United States)

    Dieni, Cristina V; Ferraresi, Aldo; Sullivan, Jacqueline A; Grassi, Sivarosa; Pettorossi, Vito E; Panichi, Roberto

    2018-03-01

    The vestibulo-ocular reflex (VOR) adaptation is an ideal model for investigating how the neurosteroid 17 beta-estradiol (E2) contributes to the modification of behavior by regulating synaptic activities. We hypothesized that E2 impacts VOR adaptation by affecting cerebellar synaptic plasticity at the parallel fiber-Purkinje cell (PF) synapse. To verify this hypothesis, we investigated the acute effect of blocking E2 synthesis on gain increases and decreases in adaptation of the VOR in male rats using an oral dose (2.5 mg/kg) of the aromatase inhibitor letrozole. We also assessed the effect of letrozole on synaptic plasticity at the PF synapse in vitro, using cerebellar slices from male rats. We found that letrozole acutely impaired both gain increases and decreases adaptation of the VOR without altering basal ocular-motor performance. Moreover, letrozole prevented long-term potentiation at the PF synapse (PF-LTP) without affecting long-term depression (PF-LTD). Thus, in male rats neurosteroid E2 has a relevant impact on VOR adaptation and affects exclusively PF-LTP. These findings suggest that E2 might regulate changes in VOR adaptation by acting locally on cerebellar and extra-cerebellar synaptic plasticity sites.

  1. Exact estimation of biodiesel cetane number (CN) from its fatty acid methyl esters (FAMEs) profile using partial least square (PLS) adapted by artificial neural network (ANN)

    International Nuclear Information System (INIS)

    Hosseinpour, Soleiman; Aghbashlo, Mortaza; Tabatabaei, Meisam; Khalife, Esmail

    2016-01-01

    Highlights: • Estimating the biodiesel CN from its FAMEs profile using ANN-based PLS approach. • Comparing the capability of ANN-adapted PLS approach with the standard PLS model. • Exact prediction of biodiesel CN from it FAMEs profile using ANN-based PLS method. • Developing an easy-to-use software using ANN-PLS model for computing the biodiesel CN. - Abstract: Cetane number (CN) is among the most important properties of biodiesel because it quantifies combustion speed or in better words, ignition quality. Experimental measurement of biodiesel CN is rather laborious and expensive. However, the high proportionality of biodiesel fatty acid methyl esters (FAMEs) profile with its CN is very appealing to develop straightforward and inexpensive computerized tools for biodiesel CN estimation. Unfortunately, correlating the chemical structure of biodiesel to its CN using conventional statistical and mathematical approaches is very difficult. To solve this issue, partial least square (PLS) adapted by artificial neural network (ANN) was introduced and examined herein as an innovative approach for the exact estimation of biodiesel CN from its FAMEs profile. In the proposed approach, ANN paradigm was used for modeling the inner relation between the input and the output PLS score vectors. In addition, the capability of the developed method in predicting the biodiesel CN was compared with the basal PLS method. The accuracy of the developed approaches for computing the biodiesel CN was assessed using three statistical criteria, i.e., coefficient of determination (R"2), mean-squared error (MSE), and percentage error (PE). The ANN-adapted PLS method predicted the biodiesel CN with an R"2 value higher than 0.99 demonstrating the fidelity of the developed model over the classical PLS method with a markedly lower R"2 value of about 0.85. In order to facilitate the use of the proposed model, an easy-to-use computer program was also developed on the basis of ANN-adapted PLS

  2. Engineering Human Neural Tissue by 3D Bioprinting.

    Science.gov (United States)

    Gu, Qi; Tomaskovic-Crook, Eva; Wallace, Gordon G; Crook, Jeremy M

    2018-01-01

    Bioprinting provides an opportunity to produce three-dimensional (3D) tissues for biomedical research and translational drug discovery, toxicology, and tissue replacement. Here we describe a method for fabricating human neural tissue by 3D printing human neural stem cells with a bioink, and subsequent gelation of the bioink for cell encapsulation, support, and differentiation to functional neurons and supporting neuroglia. The bioink uniquely comprises the polysaccharides alginate, water-soluble carboxymethyl-chitosan, and agarose. Importantly, the method could be adapted to fabricate neural and nonneural tissues from other cell types, with the potential to be applied for both research and clinical product development.

  3. Assessment of students’ health condition by indicators of adaptation potential, biological age and bio-energetic reserves of organism

    Directory of Open Access Journals (Sweden)

    O.V. Martyniuk

    2015-06-01

    Full Text Available Purpose: to assess students’ health condition by indicators of adaptation potential, biological age and express-assessment. Material: in the research 47 first and second year girl students participated, who belonged to main health group. Results: we distributed the girl students into three groups: 14.89% of them were included in group with “safe” health condition; 34.04% - in group of “third state”; 51.06% were related to group with “ dangerous” health condition. We established that dangerous level was characterized by energy potential of below middle and low level. It is accompanied by accelerated processes of organism’s age destructions and tension of regulation mechanisms. Conclusions: the received results permit to further develop and generalize the data of students’ health’s assessment by indicators of adaptation potentials, biological age and physical health’s condition.

  4. Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation

    Energy Technology Data Exchange (ETDEWEB)

    Entchev, Evgueniy; Yang, Libing [Integrated Energy Systems Laboratory, CANMET Energy Technology Centre, 1 Haanel Dr., Ottawa, Ontario (Canada)

    2007-06-30

    This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5 kW{sub el} SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models' predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models' accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system's performance could be modelled with minimum time demand and with a high degree of accuracy. (author)

  5. arXiv The prototype of the HL-LHC magnets monitoring system based on Recurrent Neural Networks and adaptive quantization

    CERN Document Server

    Wielgosz, Maciej; Skoczeń, Andrzej

    This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, a...

  6. A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

    Science.gov (United States)

    Xu, Rui; Zhou, Miaolei

    2018-04-01

    Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

  7. Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation

    Directory of Open Access Journals (Sweden)

    Ramin Jaberi

    2017-12-01

    Full Text Available Purpose : Intra-fractional organs at risk (OARs deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT. The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Material and methods : Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. Results : A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in ‘organs-applicators’, while maintaining target dose at the original level. Conclusions : There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients’ plans to be able to serve as a clinical tool.

  8. Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation.

    Science.gov (United States)

    Jaberi, Ramin; Siavashpour, Zahra; Aghamiri, Mahmoud Reza; Kirisits, Christian; Ghaderi, Reza

    2017-12-01

    Intra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria. Thirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs) based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan. A model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in 'organs-applicators', while maintaining target dose at the original level. There are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients' plans to be able to serve as a clinical tool.

  9. Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning.

    Science.gov (United States)

    Grossberg, Stephen; Kishnan, Devika

    2018-01-01

    This article develops the iSTART neural model that proposes how specific imbalances in cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, and cerebellum may interact together to cause behavioral symptoms of autism. These imbalances include underaroused emotional depression in the amygdala/hypothalamus, learning of hyperspecific recognition categories that help to cause narrowly focused attention in temporal and prefrontal cortices, and breakdowns of adaptively timed motivated attention and motor circuits in the hippocampus and cerebellum. The article expands the model's explanatory range by, first, explaining recent data about Fragile X syndrome (FXS), mGluR, and trace conditioning; and, second, by explaining distinct causes of stereotyped behaviors in individuals with autism. Some of these stereotyped behaviors, such as an insistence on sameness and circumscribed interests, may result from imbalances in the cognitive and emotional circuits that iSTART models. These behaviors may be ameliorated by operant conditioning methods. Other stereotyped behaviors, such as repetitive motor behaviors, may result from imbalances in how the direct and indirect pathways of the basal ganglia open or close movement gates, respectively. These repetitive behaviors may be ameliorated by drugs that augment D2 dopamine receptor responses or reduce D1 dopamine receptor responses. The article also notes the ubiquitous role of gating by basal ganglia loops in regulating all the functions that iSTART models.

  10. Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

    Directory of Open Access Journals (Sweden)

    Stephen Grossberg

    2018-03-01

    Full Text Available This article develops the iSTART neural model that proposes how specific imbalances in cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, and cerebellum may interact together to cause behavioral symptoms of autism. These imbalances include underaroused emotional depression in the amygdala/hypothalamus, learning of hyperspecific recognition categories that help to cause narrowly focused attention in temporal and prefrontal cortices, and breakdowns of adaptively timed motivated attention and motor circuits in the hippocampus and cerebellum. The article expands the model’s explanatory range by, first, explaining recent data about Fragile X syndrome (FXS, mGluR, and trace conditioning; and, second, by explaining distinct causes of stereotyped behaviors in individuals with autism. Some of these stereotyped behaviors, such as an insistence on sameness and circumscribed interests, may result from imbalances in the cognitive and emotional circuits that iSTART models. These behaviors may be ameliorated by operant conditioning methods. Other stereotyped behaviors, such as repetitive motor behaviors, may result from imbalances in how the direct and indirect pathways of the basal ganglia open or close movement gates, respectively. These repetitive behaviors may be ameliorated by drugs that augment D2 dopamine receptor responses or reduce D1 dopamine receptor responses. The article also notes the ubiquitous role of gating by basal ganglia loops in regulating all the functions that iSTART models.

  11. Toward a new task assignment and path evolution (TAPE) for missile defense system (MDS) using intelligent adaptive SOM with recurrent neural networks (RNNs).

    Science.gov (United States)

    Wang, Chi-Hsu; Chen, Chun-Yao; Hung, Kun-Neng

    2015-06-01

    In this paper, a new adaptive self-organizing map (SOM) with recurrent neural network (RNN) controller is proposed for task assignment and path evolution of missile defense system (MDS). We address the problem of N agents (defending missiles) and D targets (incoming missiles) in MDS. A new RNN controller is designed to force an agent (or defending missile) toward a target (or incoming missile), and a monitoring controller is also designed to reduce the error between RNN controller and ideal controller. A new SOM with RNN controller is then designed to dispatch agents to their corresponding targets by minimizing total damaging cost. This is actually an important application of the multiagent system. The SOM with RNN controller is the main controller. After task assignment, the weighting factors of our new SOM with RNN controller are activated to dispatch the agents toward their corresponding targets. Using the Lyapunov constraints, the weighting factors for the proposed SOM with RNN controller are updated to guarantee the stability of the path evolution (or planning) system. Excellent simulations are obtained using this new approach for MDS, which show that our RNN has the lowest average miss distance among the several techniques.

  12. Enhanced viability and neural differential potential in poor post-thaw hADSCs by agarose multi-well dishes and spheroid culture.

    Science.gov (United States)

    Guo, Xiaoling; Li, Shanyi; Ji, Qingshan; Lian, Ruiling; Chen, Jiansu

    2015-10-01

    Human adipose-derived stem cells (hADSCs) are potential adult stem cells source for cell therapy. But hADSCs with multi-passage or cryopreservation often revealed poor growth performance. The aim of our work was to improve the activity of poor post-thaw hADSCs by simple and effective means. We describe here a simple method based on commercially available silicone micro-wells for creating hADSCs spheroids to improve viability and neural differentiation potential on poor post-thaw hADSCs. The isolated hADSCs positively expresse d CD29, CD44, CD105, and negatively expressed CD34, CD45, HLA-DR by flow cytometry. Meanwhile, they had adipogenic and osteogenic differentiation capacity. The post-thaw and post-spheroid hADSCs from poor growth status hADSCs showed a marked increase in cell proliferation by CKK-8 analysis, cell cycle analysis and Ki67/P27 quantitative polymerase chain reaction (qPCR) analysis. They also displayed an increase viability of anti-apoptosis by annexin v and propidium iodide assays and mitochondrial membrane potential assays. After 3 days of neural induction, the neural differentiation potential of post-thaw and post-spheroid hADSCs could be enhanced by qPCR analysis and western blotting analysis. These results suggested that the spheroid formation could improve the viability and neural differentiation potential of bad growth status hADSCs, which is conducive to ADSCs research and cell therapy.

  13. Climate change and nesting behaviour in vertebrates: a review of the ecological threats and potential for adaptive responses.

    Science.gov (United States)

    Mainwaring, Mark C; Barber, Iain; Deeming, Denis C; Pike, David A; Roznik, Elizabeth A; Hartley, Ian R

    2017-11-01

    Nest building is a taxonomically widespread and diverse trait that allows animals to alter local environments to create optimal conditions for offspring development. However, there is growing evidence that climate change is adversely affecting nest-building in animals directly, for example via sea-level rises that flood nests, reduced availability of building materials, and suboptimal sex allocation in species exhibiting temperature-dependent sex determination. Climate change is also affecting nesting species indirectly, via range shifts into suboptimal nesting areas, reduced quality of nest-building environments, and changes in interactions with nest predators and parasites. The ability of animals to adapt to sustained and rapid environmental change is crucial for the long-term persistence of many species. Many animals are known to be capable of adjusting nesting behaviour adaptively across environmental gradients and in line with seasonal changes, and this existing plasticity potentially facilitates adaptation to anthropogenic climate change. However, whilst alterations in nesting phenology, site selection and design may facilitate short-term adaptations, the ability of nest-building animals to adapt over longer timescales is likely to be influenced by the heritable basis of such behaviour. We urgently need to understand how the behaviour and ecology of nest-building in animals is affected by climate change, and particularly how altered patterns of nesting behaviour affect individual fitness and population persistence. We begin our review by summarising how predictable variation in environmental conditions influences nest-building animals, before highlighting the ecological threats facing nest-building animals experiencing anthropogenic climate change and examining the potential for changes in nest location and/or design to provide adaptive short- and long-term responses to changing environmental conditions. We end by identifying areas that we believe warrant the

  14. Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

    Science.gov (United States)

    Allison, Thomas C

    2016-03-03

    Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 (•)OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which (•)OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.

  15. Stronger efferent suppression of cochlear neural potentials by contralateral acoustic stimulation in awake than in anesthetized chinchilla

    Directory of Open Access Journals (Sweden)

    Cristian eAedo

    2015-03-01

    Full Text Available There are two types of sensory cells in the mammalian cochlea, inner hair cells, which make synaptic contact with auditory-nerve afferent fibers, and outer hair cells that are innervated by crossed and uncrossed medial olivocochlear (MOC efferent fibers. Contralateral acoustic stimulation activates the uncrossed efferent MOC fibers reducing cochlear neural responses, thus modifying the input to the central auditory system. The chinchilla, among all studied mammals, displays the lowest percentage of uncrossed MOC fibers raising questions about the strength and frequency distribution of the contralateral-sound effect in this species. On the other hand, MOC effects on cochlear sensitivity have been mainly studied in anesthetized animals and since the MOC-neuron activity depends on the level of anesthesia, it is important to assess the influence of anesthesia in the strength of efferent effects. Seven adult chinchillas (Chinchilla laniger were chronically implanted with round-window electrodes in both cochleae. We compared the effect of contralateral sound in awake and anesthetized condition. Compound action potentials (CAP and cochlear microphonics (CM were measured in the ipsilateral cochlea in response to tones in absence and presence of contralateral sound. Control measurements performed after middle-ear muscles section in one animal discarded any possible middle-ear reflex activation. Contralateral sound produced CAP amplitude reductions in all chinchillas, with suppression effects greater by about 1-3 dB in awake than in anesthetized animals. In contrast, CM amplitude increases of up to 1.9 dB were found in only three awake chinchillas. In both conditions the strongest efferent effects were produced by contralateral tones at frequencies equal or close to those of ipsilateral tones. Contralateral CAP suppressions for 1-6 kHz ipsilateral tones corresponded to a span of uncrossed MOC fiber innervation reaching at least the central third of the

  16. Stronger efferent suppression of cochlear neural potentials by contralateral acoustic stimulation in awake than in anesthetized chinchilla.

    Science.gov (United States)

    Aedo, Cristian; Tapia, Eduardo; Pavez, Elizabeth; Elgueda, Diego; Delano, Paul H; Robles, Luis

    2015-01-01

    There are two types of sensory cells in the mammalian cochlea, inner hair cells, which make synaptic contact with auditory-nerve afferent fibers, and outer hair cells that are innervated by crossed and uncrossed medial olivocochlear (MOC) efferent fibers. Contralateral acoustic stimulation activates the uncrossed efferent MOC fibers reducing cochlear neural responses, thus modifying the input to the central auditory system. The chinchilla, among all studied mammals, displays the lowest percentage of uncrossed MOC fibers raising questions about the strength and frequency distribution of the contralateral-sound effect in this species. On the other hand, MOC effects on cochlear sensitivity have been mainly studied in anesthetized animals and since the MOC-neuron activity depends on the level of anesthesia, it is important to assess the influence of anesthesia in the strength of efferent effects. Seven adult chinchillas (Chinchilla laniger) were chronically implanted with round-window electrodes in both cochleae. We compared the effect of contralateral sound in awake and anesthetized condition. Compound action potentials (CAP) and cochlear microphonics (CM) were measured in the ipsilateral cochlea in response to tones in absence and presence of contralateral sound. Control measurements performed after middle-ear muscles section in one animal discarded any possible middle-ear reflex activation. Contralateral sound produced CAP amplitude reductions in all chinchillas, with suppression effects greater by about 1-3 dB in awake than in anesthetized animals. In contrast, CM amplitude increases of up to 1.9 dB were found in only three awake chinchillas. In both conditions the strongest efferent effects were produced by contralateral tones at frequencies equal or close to those of ipsilateral tones. Contralateral CAP suppressions for 1-6 kHz ipsilateral tones corresponded to a span of uncrossed MOC fiber innervation reaching at least the central third of the chinchilla cochlea.

  17. Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong

    International Nuclear Information System (INIS)

    Lu, W.-Z.; Wang, W.-J.; Wang, X.-K.; Yan, S.-H.; Lam, Joseph C.

    2004-01-01

    The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NO x ), and nitrogen dioxide (NO 2 ) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NO x and NO 2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods

  18. Can Older Adults Resist the Positivity Effect in Neural Responding: The Impact of Verbal Framing on Event-Related Brain Potentials Elicited by Emotional Images

    OpenAIRE

    Rehmert, Andrea E.; Kisley, Michael A.

    2013-01-01

    Older adults have demonstrated an avoidance of negative information presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice, or an involuntary, automatic response will be important to differentiate, as decision-making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural respon...

  19. Potential of adaptive clinical trial designs in pharmacogenetic research, A simulation based on the IPASS trial

    NARCIS (Netherlands)

    Van Der Baan, Frederieke H.; Knol, Mirjam J.|info:eu-repo/dai/nl/304820350; Klungel, Olaf H.|info:eu-repo/dai/nl/181447649; Egberts, Toine C.G.|info:eu-repo/dai/nl/162850050; Grobbee, Diederick E.; Roes, Kit C.B.

    2011-01-01

    Background: An adaptive clinical trial design that allows population enrichment after interim analysis can be advantageous in pharmacogenetic research if previous evidence is not strong enough to exclude part of the patient population beforehand.With this design, underpowered studies or unnecessary

  20. Difference, Adapted Physical Activity and Human Development: Potential Contribution of Capabilities Approach

    Science.gov (United States)

    Silva, Carla Filomena; Howe, P. David

    2012-01-01

    This paper is a call to Adapted Physical Activity (APA) professionals to increase the reflexive nature of their practice. Drawing upon Foucault's concept of governmentality (1977) APA action may work against its own publicized goals of empowerment and self-determination. To highlight these inconsistencies, we will draw upon historical and social…

  1. Energy saving potential of long-term climate adaptive greenhouse shells

    NARCIS (Netherlands)

    Lee, C.; Costola, D.; Loonen, R.C.G.M.; Hensen, J.L.M.

    2013-01-01

    This paper describes yearly and monthly optimization of greenhouse shells. Simulations adopt a validated building energy simulation program, adapted and re-validated for simulation of commercial greenhouses, including a tomato crop model. The work focuses on multi-objective optimization of thermal

  2. Potential contribution of natural enemies to patterns of local adaptation in plants

    Czech Academy of Sciences Publication Activity Database

    Crémieux, L.; Bischoff, A.; Šmilauerová, M.; Lawson, C.S.; Mortimer, S. R.; Doležal, Jiří; Lanta, Vojtěch; Edwards, A.R.; Brook, A.J.; Tscheulin, T.; Macel, M.; Lepš, Jan; Müller-Schärer, H.; Steinger, T.

    2008-01-01

    Roč. 180, č. 2 (2008), s. 524-533 ISSN 0028-646X Grant - others:EU(XE) EVK-2-CT-1999-00032 Institutional research plan: CEZ:AV0Z60050516; CEZ:AV0Z50070508 Keywords : local adaptation * plant-parasite interaction * pathogen Subject RIV: EF - Botanics Impact factor: 5.178, year: 2008

  3. Possible impacts of sea level rise on disease transmission and potential adaptation strategies, a review.

    Science.gov (United States)

    Dvorak, Ana C; Solo-Gabriele, Helena M; Galletti, Andrea; Benzecry, Bernardo; Malone, Hannah; Boguszewski, Vicki; Bird, Jason

    2018-04-18

    Sea levels are projected to rise in response to climate change, causing the intrusion of sea water into land. In flat coastal regions, this would generate an increase in shallow water covered areas with limited circulation. This scenario raises a concern about the consequences it could have on human health, specifically the possible impacts on disease transmission. In this review paper we identified three categories of diseases which are associated with water and whose transmission can be affected by sea level rise. These categories include: mosquitoborne diseases, naturalized organisms (Vibrio spp. and toxic algae), and fecal-oral diseases. For each disease category, we propose comprehensive adaptation strategies that would help minimize possible health risks. Finally, the City of Key West, Florida is analyzed as a case study, due to its inherent vulnerability to sea level rise. Current and projected adaptation techniques are discussed as well as the integration of additional recommendations, focused on disease transmission control. Given that sea level rise will likely continue into the future, the promotion and implementation of positive adaptation strategies is necessary to ensure community resilience. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. The potential for adaptive maintenance of diversity in insect antimicrobial peptides.

    Science.gov (United States)

    Unckless, Robert L; Lazzaro, Brian P

    2016-05-26

    Genes involved in immune defence are among the fastest evolving in the genomes of many species. Interestingly, however, genes encoding antimicrobial peptides (AMPs) have shown little evidence for adaptive divergence in arthropods, despite the centrality of these peptides in direct killing of microbial pathogens. This observation, coupled with a failure to detect phenotypic consequence of genetic variation in AMPs, has led to the hypothesis that individual AMPs make minor contributions to overall immune defence and that AMPs instead act as a collective cocktail. Recent data, however, have suggested an alternative explanation for the apparent lack of adaptive divergence in AMP genes. Molecular evolutionary and phenotypic data have begun to suggest that variant AMP alleles may be maintained through balancing selection in invertebrates, a pattern similar to that observed in several vertebrate AMPs. Signatures of balancing selection include high rates of non-synonymous polymorphism, trans-species amino acid polymorphisms, and convergence of amino acid states across the phylogeny. In this review, we revisit published literature on insect AMP genes and analyse newly available population genomic datasets in Drosophila, finding enrichment for patterns consistent with adaptive maintenance of polymorphism.This article is part of the themed issue 'Evolutionary ecology of arthropod antimicrobial peptides'. © 2016 The Authors.

  5. Can older adults resist the positivity effect in neural responding? The impact of verbal framing on event-related brain potentials elicited by emotional images.

    Science.gov (United States)

    Rehmert, Andrea E; Kisley, Michael A

    2013-10-01

    Older adults have demonstrated an avoidance of negative information, presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice or an involuntary, automatic response will be important to differentiate, as decision making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural responses to negative stimuli through verbal reframing of evaluative response options. The late positive potential (LPP) response of 50 older adults and 50 younger adults was recorded while participants categorized emotional images in one of two framing conditions: positive ("more or less positive") or negative ("more or less negative"). It was hypothesized that older adults would be able to overcome a presumed tendency to down-regulate neural responding to negative stimuli in the negative framing condition, thus leading to larger LPP wave amplitudes to negative images. A similar effect was predicted for younger adults, but for positively valenced images, such that LPP responses would be increased in the positive framing condition compared with the negative framing condition. Overall, younger adults' LPP wave amplitudes were modulated by framing condition, including a reduction in the negativity bias in the positive frame. Older adults' neural responses were not significantly modulated, even though task-related behavior supported the notion that older adults were able to successfully adopt the negative framing condition.

  6. Can Older Adults Resist the Positivity Effect in Neural Responding: The Impact of Verbal Framing on Event-Related Brain Potentials Elicited by Emotional Images

    Science.gov (United States)

    Rehmert, Andrea E.; Kisley, Michael A.

    2014-01-01

    Older adults have demonstrated an avoidance of negative information presumably with a goal of greater emotional satisfaction. Understanding whether avoidance of negative information is a voluntary, motivated choice, or an involuntary, automatic response will be important to differentiate, as decision-making often involves emotional factors. With the use of an emotional framing event-related potential (ERP) paradigm, the present study investigated whether older adults could alter neural responses to negative stimuli through verbal reframing of evaluative response options. The late-positive potential (LPP) response of 50 older adults and 50 younger adults was recorded while participants categorized emotional images in one of two framing conditions: positive (“more or less positive”) or negative (“more or less negative”). It was hypothesized that older adults would be able to overcome a presumed tendency to down-regulate neural responding to negative stimuli in the negative framing condition thus leading to larger LPP wave amplitudes to negative images. A similar effect was predicted for younger adults but for positively valenced images such that LPP responses would be increased in the positive framing condition compared to the negative framing condition. Overall, younger adults' LPP wave amplitudes were modulated by framing condition, including a reduction in the negativity bias in the positive frame. Older adults' neural responses were not significantly modulated even though task-related behavior supported the notion that older adults were able to successfully adopt the negative framing condition. PMID:23731435

  7. Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

    Science.gov (United States)

    Kang, Jin Kyu; Hong, Hyung Gil; Park, Kang Ryoung

    2017-07-08

    A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

  8. Phylogeography, salinity adaptations and metabolic potential of the Candidate Division KB1 Bacteria based on a partial single cell genome.

    Directory of Open Access Journals (Sweden)

    Lisa M Nigro

    2016-08-01

    Full Text Available Deep-sea hypersaline anoxic basins (DHABs and other hypersaline environments contain abundant and diverse microbial life that has adapted to these extreme conditions. The bacterial Candidate Division KB1 represents one of several uncultured groups that has been consistently observed in hypersaline microbial diversity studies. Here we report the phylogeography of KB1, its phylogenetic relationships to Candidate Division OP1 Bacteria, and its potential metabolic and osmotic stress adaptations based on a partial single cell amplified genome (SAG of KB1 from Orca Basin, the largest hypersaline seafloor brine basin in the Gulf of Mexico. Our results are consistent with the hypothesis – previously developed based on 14C incorporation experiments with mixed-species enrichments from Mediterranean seafloor brines - that KB1 has adapted its proteins to elevated intracellular salinity, but at the same time KB1 apparently imports glycine betaine; this compatible solute is potentially not limited to osmoregulation but could also serve as a carbon and energy source.

  9. Extensive copy number variations in admixed Indian population of African ancestry: potential involvement in adaptation.

    Science.gov (United States)

    Narang, Ankita; Jha, Pankaj; Kumar, Dhirendra; Kutum, Rintu; Mondal, Anupam Kumar; Dash, Debasis; Mukerji, Mitali

    2014-11-13

    Admixture mapping has been enormously resourceful in identifying genetic variations linked to phenotypes, adaptation, and diseases. In this study through analysis of copy number variable regions (CNVRs), we report extensive restructuring in the genomes of the recently admixed African-Indian population (OG-W-IP) that inhabits a highly saline environment in Western India. The study included subjects from OG-W-IP (OG), five different Indian and three HapMap populations that were genotyped using Affymetrix version 6.0 arrays. Copy number variations (CNVs) detected using Birdsuite were used to define CNVRs. Population structure with respect to CNVRs was delineated using random forest approach. OG genomes have a surprising excess of CNVs in comparison to other studied populations. Individual ancestry proportions computed using STRUCTURE also reveals a unique genetic component in OGs. Population structure analysis with CNV genotypes indicates OG to be distant from both the African and Indian ancestral populations. Interestingly, it shows genetic proximity with respect to CNVs to only one Indian population IE-W-LP4, which also happens to reside in the same geographical region. We also observe a significant enrichment of molecular processes related to ion binding and receptor activity in genes encompassing OG-specific CNVRs. Our results suggest that retention of CNVRs from ancestral natives and de novo acquisition of CNVRs could accelerate the process of adaptation especially in an extreme environment. Additionally, this population would be enormously useful for dissecting genes and delineating the involvement of CNVs in salt adaptation. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  10. Polyextremotolerant black fungi: oligotrophism, adaptive potential and a link to lichen symbioses

    Directory of Open Access Journals (Sweden)

    Cene eGostinčar

    2012-11-01

    Full Text Available Black meristematic fungi can survive high doses of radiation and are resistant to desiccation. These adaptations help them to colonize harsh oligotrophic habitats, e.g. on the surface and subsurface of rocks. One of their most characteristic stress-resistance mechanisms is the accumulation of melanin in the cell walls. This, production of other protective molecules and a plastic morphology further contribute to ecological flexibility of black fungi. Increased growth rates of some species after exposure to ionizing radiation even suggest yet unknown mechanisms of energy production. Other unusual metabolic strategies may include harvesting UV or visible light or gaining energy by forming facultative lichen-like associations with algae or cyanobacteria. The latter is not entirely surprising, since certain black fungal lineages are phylogenetically related to clades of lichen-forming fungi. Similar to black fungi, lichen-forming fungi are adapted to growth on exposed surfaces with low availability of nutrients. They also efficiently use protective molecules to tolerate frequent periods of extreme stress. Traits shared by both groups of fungi may have been important in facilitating the evolution and radiation of lichen-symbioses.

  11. Adapted physical exercise enhances activation and differentiation potential of satellite cells in the skeletal muscle of old mice.

    Science.gov (United States)

    Cisterna, Barbara; Giagnacovo, Marzia; Costanzo, Manuela; Fattoretti, Patrizia; Zancanaro, Carlo; Pellicciari, Carlo; Malatesta, Manuela

    2016-05-01

    During ageing, a progressive loss of skeletal muscle mass and a decrease in muscle strength and endurance take place, in the condition termed sarcopenia. The mechanisms of sarcopenia are complex and still unclear; however, it is known that muscle atrophy is associated with a decline in the number and/or efficiency of satellite cells, the main contributors to muscle regeneration. Physical exercise proved beneficial in sarcopenia; however, knowledge of the effect of adapted physical exercise on the myogenic properties of satellite cells in aged muscles is limited. In this study the amount and activation state of satellite cells as well as their proliferation and differentiation potential were assessed in situ by morphology, morphometry and immunocytochemistry at light and transmission electron microscopy on 28-month-old mice submitted to adapted aerobic physical exercise on a treadmill. Sedentary age-matched mice served as controls, and sedentary adult mice were used as a reference for an unperturbed control at an age when the capability of muscle regeneration is still high. The effect of physical exercise in aged muscles was further analysed by comparing the myogenic potential of satellite cells isolated from old running and old sedentary mice using an in vitro system that allows observation of the differentiation process under controlled experimental conditions. The results of this ex vivo and in vitro study demonstrated that adapted physical exercise increases the number and activation of satellite cells as well as their capability to differentiate into structurally and functionally correct myotubes (even though the age-related impairment in myotube formation is not fully reversed): this evidence further supports adapted physical exercise as a powerful, non-pharmacological approach to counteract sarcopenia and the age-related deterioration of satellite cell capabilities even at very advanced age. © 2016 Anatomical Society.