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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. Neural correlates of adaptation to voice identity.

    Science.gov (United States)

    Schweinberger, Stefan R; Walther, Christian; Zäske, Romi; Kovács, Gyula

    2011-11-01

    Apart from speech content, the human voice also carries paralinguistic information about speaker identity. Voice identification and its neural correlates have received little scientific attention up to now. Here we use event-related potentials (ERPs) in an adaptation paradigm, in order to investigate the neural representation and the time course of vocal identity processing. Participants adapted to repeated utterances of vowel-consonant-vowel (VCV) of one personally familiar speaker (either A or B), before classifying a subsequent test voice varying on an identity continuum between these two speakers. Following adaptation to speaker A, test voices were more likely perceived as speaker B and vice versa, and these contrastive voice identity aftereffects (VIAEs) were much more pronounced when the same syllable, rather than a different syllable, was used as adaptor. Adaptation induced amplitude reductions of the frontocentral N1-P2 complex and a prominent reduction of the parietal P3 component, for test voices preceded by identity-corresponding adaptors. Importantly, only the P3 modulation remained clear for across-syllable combinations of adaptor and test stimuli. Our results suggest that voice identity is contrastively processed by specialized neurons in auditory cortex within ∼250 ms after stimulus onset, with identity processing becoming less dependent on speech content after ∼300 ms. ©2011 The British Psychological Society.

  3. 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.

  4. Nonlinear adaptive neural controller for unstable aircraft

    OpenAIRE

    Suresh, S; Omkar, SN; Mani, V; Sundararajan, N

    2005-01-01

    A model reference indirect adaptive neural control scheme that uses both off-line and online learning strategies is proposed for an,unstable nonlinear aircraft controller design. The bounded-input-bounded-output stability requirement for the controller design is circumvented using an off-line, finite interval of time training scheme. The aircraft model is first identified using a neural network with linear filter (also known as time-delayed neural network) with the available input-output data...

  5. 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

  6. Flight control with adaptive critic neural network

    Science.gov (United States)

    Han, Dongchen

    2001-10-01

    In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

  7. Neural network with dynamically adaptable neurons

    Science.gov (United States)

    Tawel, Raoul (Inventor)

    1994-01-01

    This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.

  8. 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.

  9. 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. Furthermore...

  10. Adaptive Neurotechnology for Making Neural Circuits Functional .

    Science.gov (United States)

    Jung, Ranu

    2008-03-01

    Two of the most important trends in recent technological developments are that technology is increasingly integrated with biological systems and that it is increasingly adaptive in its capabilities. Neuroprosthetic systems that provide lost sensorimotor function after a neural disability offer a platform to investigate this interplay between biological and engineered systems. Adaptive neurotechnology (hardware and software) could be designed to be biomimetic, guided by the physical and programmatic constraints observed in biological systems, and allow for real-time learning, stability, and error correction. An example will present biomimetic neural-network hardware that can be interfaced with the isolated spinal cord of a lower vertebrate to allow phase-locked real-time neural control. Another will present adaptive neural network control algorithms for functional electrical stimulation of the peripheral nervous system to provide desired movements of paralyzed limbs in rodents or people. Ultimately, the frontier lies in being able to utilize the adaptive neurotechnology to promote neuroplasticity in the living system on a long-time scale under co-adaptive conditions.

  11. Adaptive neural control of aeroelastic response

    Science.gov (United States)

    Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.

    1996-05-01

    The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.

  12. A neural systems analysis of adaptive navigation.

    Science.gov (United States)

    Mizumori, S J; Cooper, B G; Leutgeb, S; Pratt, W E

    2000-01-01

    In the field of the neurobiology of learning, significant emphasis has been placed on understanding neural plasticity within a single structure (or synapse type) as it relates to a particular type of learning mediated by a particular brain area. To appreciate fully the breadth of the plasticity responsible for complex learning phenomena, it is imperative that we also examine the neural mechanisms of the behavioral instantiation of learned information, how motivational systems interact, and how past memories affect the learning process. To address this issue, we describe a model of complex learning (rodent adaptive navigation) that could be used to study dynamically interactive neural systems. Adaptive navigation depends on the efficient integration of external and internal sensory information with motivational systems to arrive at the most effective cognitive and/or behavioral strategies. We present evidence consistent with the view that during navigation: 1) the limbic thalamus and limbic cortex is primarily responsible for the integration of current and expected sensory information, 2) the hippocampal-septal-hypothalamic system provides a mechanism whereby motivational perspectives bias sensory processing, and 3) the amygdala-prefrontal-striatal circuit allows animals to evaluate the expected reinforcement consequences of context-dependent behavioral responses. Although much remains to be determined regarding the nature of the interactions among neural systems, new insights have emerged regarding the mechanisms that underlie flexible and adaptive behavioral responses.

  13. Evaluation of a Neural Adaptive Flight Controller

    Science.gov (United States)

    Totah, Joseph J.

    1997-01-01

    The objective of this paper is to present results from the evaluation of a direct adaptive tracking controller. The control architecture employs both pre-trained and an on-line neural networks to represent the non-linear aircraft dynamics in the model inversion portion of the controller. The aircraft model used for this evaluation is representative of the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft. The controller was evaluated for three cases: (1) nominal conditions; (2) loss of control power; and (3) loss of control power in the presence of atmospheric turbulence. The results were compared with the existing F-15 ACTIVE conventional mode controller in all cases. The results indicate extremely desirable airframe stabilization characteristics for case (1) that do not degrade significantly for case (2) or (3) as does the conventional mode controller. It was concluded that this controller exhibits both stable and robust adaptive characteristics when subjected to mild and extreme loss of control power conditions. Integration of this neural adaptive flight controller into the full non-linear six degree-of-freedom F-15 ACTIVE simulation is recommended for evaluation in a real-time high fidelity piloted simulation environment.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

    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...... that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines....

  19. 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.

  20. 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.)

  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. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  5. Control Augmentation Using Adaptive Fuzzy Neural Networks

    Science.gov (United States)

    Kato, Akio; Wada, Yoshihisa

    Control to improve control characteristics of aircraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. When the control laws using fuzzy inference were designed, trial and error was repeated for optimization of the parameter. Here, in designing control laws using fuzzy neural networks, the systematic optimization of the parameter was possible using the learning algorithm usually used in neural networks, by expressing the fuzzy inference in the form of neural networks. Here, the control laws, which learned the characteristics of the aircraft for one flight condition only, were used in all flight conditions without changing any parameter. Evaluation of the designed control laws showed good performance in all flight conditions. This proves that fuzzy neural networks are an effective and flexible method when applied to control laws for control augmentation of aircraft.

  6. Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.

    Science.gov (United States)

    Zhang, Yanjun; Tao, Gang; Chen, Mou

    2016-09-01

    This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.

  7. Online adaptive neural control of a robotic lower limb prosthesis

    Science.gov (United States)

    Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

    2018-02-01

    Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

  8. Adaptive Optimization of Aircraft Engine Performance Using Neural Networks

    Science.gov (United States)

    Simon, Donald L.; Long, Theresa W.

    1995-01-01

    Preliminary results are presented on the development of an adaptive neural network based control algorithm to enhance aircraft engine performance. This work builds upon a previous National Aeronautics and Space Administration (NASA) effort known as Performance Seeking Control (PSC). PSC is an adaptive control algorithm which contains a model of the aircraft's propulsion system which is updated on-line to match the operation of the aircraft's actual propulsion system. Information from the on-line model is used to adapt the control system during flight to allow optimal operation of the aircraft's propulsion system (inlet, engine, and nozzle) to improve aircraft engine performance without compromising reliability or operability. Performance Seeking Control has been shown to yield reductions in fuel flow, increases in thrust, and reductions in engine fan turbine inlet temperature. The neural network based adaptive control, like PSC, will contain a model of the propulsion system which will be used to calculate optimal control commands on-line. Hopes are that it will be able to provide some additional benefits above and beyond those of PSC. The PSC algorithm is computationally intensive, it is valid only at near steady-state flight conditions, and it has no way to adapt or learn on-line. These issues are being addressed in the development of the optimal neural controller. Specialized neural network processing hardware is being developed to run the software, the algorithm will be valid at steady-state and transient conditions, and will take advantage of the on-line learning capability of neural networks. Future plans include testing the neural network software and hardware prototype against an aircraft engine simulation. In this paper, the proposed neural network software and hardware is described and preliminary neural network training results are presented.

  9. Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination.

    Directory of Open Access Journals (Sweden)

    Blake T Thomas

    2015-07-01

    Full Text Available Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.e., without supervision meets this criterion. Specifically, the adaptive algorithm includes synaptogenesis, synaptic shedding, and bi-directional synaptic weight modification to produce a network with outputs (i.e. neural codes that represent input patterns proportional to the frequency of related patterns. In addition to pattern frequency, the correlational structure of the input environment also affects allocation of neural resources. The combined synaptic modification mechanisms provide an explanation of neuron allocation in the case of self-taught experts.

  10. Neural adaptations to submaximal isokinetic eccentric strength training.

    Science.gov (United States)

    Barrué-Belou, Simon; Amarantini, David; Marque, Philippe; Duclay, Julien

    2016-05-01

    This study investigated the neural adaptations following submaximal isokinetic eccentric strength training of the plantar flexors. The modulation of electromyographic (EMG) activity and spinal excitability were compared in the soleus muscle (SOL) during isometric, concentric and eccentric maximal voluntary contractions (MVC) before and after submaximal isokinetic eccentric training. Eighteen healthy subjects were divided into a training group (n = 8) and a control group (n = 10). The training protocol consisted of sixteen sessions of isokinetic eccentric strength training during 8 weeks. Normalized EMG was used to assess the activity of SOL and medial gastrocnemius muscle (MG). For SOL, maximal Hoffmann reflex (H-reflex) and compound motor potential were evoked during isometric, concentric and eccentric actions at rest (Hmax and Mmax, respectively) and during MVC (Hsup and Msup, respectively). The results showed that the torque and normalized EMG of SOL significantly increased after training during eccentric (+20.5 and +28.8 %, respectively) and isometric (+18.2 and +23.0 %, respectively) MVC (p  0.05), and remained significantly depressed during eccentric compared to isometric and concentric actions (p  0.05). These results suggested that the increase in voluntary torque after submaximal isokinetic eccentric training can be at least partly ascribed to enhanced neural drive for SOL that does not affect the H-reflex pathway.

  11. Adaptive Control for Robotic Manipulators Base on RBF Neural Network

    Directory of Open Access Journals (Sweden)

    MA Jing

    2013-09-01

    Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.

  12. Variable neural adaptive robust control: a switched system approach.

    Science.gov (United States)

    Lian, Jianming; Hu, Jianghai; Żak, Stanislaw H

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  13. Research in Neural Network Based Adaptive Control

    National Research Council Canada - National Science Library

    Calise, Anthony

    2000-01-01

    .... We regard this as a major step towards flight certification of adaptive controllers. The approach is more general in that it permits a broad class of input nonlinearities, including such effects as discrete and bang/bang control...

  14. 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.

  15. Adaptive neural network for image enhancement

    Science.gov (United States)

    Perl, Dan; Marsland, T. A.

    1992-09-01

    ANNIE is a neural network that removes noise and sharpens edges in digital images. For noise removal, ANNIE makes a weighted average of the values of the pixels over a certain neighborhood. For edge sharpening, ANNIE detects edges and applies a correction around them. Although averaging is a simple operation and needs only a two-layer neural network, detecting edges is more complex and demands several hidden layers. Based on Marr's theory of natural vision, the edge detection method uses zero-crossings in the image filtered by the ∇2G operator (where ∇2 is the Laplacian operator and G stands for a two- dimensional Gaussian distribution), and uses two channels with different spatial frequencies. Edge detectors are tuned for vertical and horizontal orientations. Lateral inhibition implemented through one-step recursion achieves both edge relaxation and correlation of the two channels. Training by means of the quickprop algorithm determines the shapes of the weighted averaging filter and the edge correction filters, and the rules for edge relaxation and channel interaction. ANNIE uses pairs of pictures as training patterns: one picture is a reference for the output of the network and the same picture deteriorated by noise and/or blur is the input of the network.

  16. 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...

  17. 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.

  18. Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

    Directory of Open Access Journals (Sweden)

    Jiao-Hong Yi

    2016-01-01

    Full Text Available Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2 are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

  19. 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.

  20. Neural network models of learning and adaptation

    Science.gov (United States)

    Denker, John S.

    1986-10-01

    Recent work has applied ideas from many fields including biology, physics and computer science, in order to understand how a highly interconnected network of simple processing elements can perform useful computation. Such networks can be used as associative memories, or as analog computers to solve optimization problems. This article reviews the workings of a standard model with particular emphasis on various schemes for learning and adaptation.

  1. Adaptive Control Using a Neural Network Estimator and Dynamic Inversion

    Science.gov (United States)

    Ninomiya, Tetsujiro; Miyazawa, Yoshikazu

    More and more UAVs are developed for various purposes and their flight controllers are required to have sufficient robustness and performance to realize their versatile missions. To design these sophisticated controller is pretty much time-consuming task by traditional design method. Neural network based adaptive control with dynamic inversion is applied to solve this problem. This method has two advantages. One is that the gain scheduling is not necessary because nonlinear dynamic inversion is applied to control nonlinear systems. The other is that neural network improves the controller performance by estimating parameters of the actual plant. Numerical examples show its effectiveness and its ability to adapt to modeling errors. This paper concludes that proposed method reduces the workload of controller design task and it has ability to adapt various errors of nonlinear systems.

  2. 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.

  3. The predictive roles of neural oscillations in speech motor adaptability.

    Science.gov (United States)

    Sengupta, Ranit; Nasir, Sazzad M

    2016-06-01

    The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations. Copyright © 2016 the American Physiological Society.

  4. Neural network based adaptive output feedback control: Applications and improvements

    Science.gov (United States)

    Kutay, Ali Turker

    Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

  5. 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.

  6. Neural Control of Chronic Stress Adaptation

    Directory of Open Access Journals (Sweden)

    James eHerman

    2013-08-01

    Full Text Available Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Adaptive nonlinear neural network controller for rotorcraft vibration

    Science.gov (United States)

    Spencer, Michael G.; Sanner, Robert M.; Chopra, Inderjit

    1997-06-01

    This paper presents research into developing an adaptive nonlinear neural network control algorithm that can be used with smart structure actuators and sensors to control the vibrations of rotor blades. The dynamic equations of motion for a blade have the same form as a multilink manipulator (robot arm) and adaptive nonlinear control algorithms have proven successful in active control of these manipulators. The recent development of neural network control algorithms has provided the ability to adaptively learn in real time a set of parameters that will approximate external forces operating on the blades. The controller combines these two control techniques enabling the controller to adapt its parameters in response to changes in blade properties such as its mass or stiffness and to also learn the parameters necessary to account for the unknown but bounded, periodic disturbance forces such as those caused by the unsteady, periodic aerodynamic forces in the rotor system. Current efforts have been directed at testing the control algorithm on real beams with piezoceramic actuators and sensors. The initial test results have shown that vibration reduction and desired beam motion tracking can be achieved even under the influences of periodic disturbances.

  12. A Comparison of Neural, Fuzzy, Evolutionary, and Adaptive Approaches for Carrier Landing

    National Research Council Canada - National Science Library

    Steinberg, Marc

    2001-01-01

    .... The control law approaches examined are: fuzzy logic, two neural network approaches, indirect adaptive and non-adaptive versions of dynamic inversion, and a hybrid approach that combines direct and indirect adaptive elements...

  13. 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

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

    Indian Academy of Sciences (India)

    2School of Computer Science and Engineering, Xinjiang University of Finance and Economics,. Urumchi 830012 ... The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is ... Keywords. Chaos control; adaptive control; adaptive identifier; fuzzy neural network; back-.

  15. Design of an adaptive neural network based power system stabilizer.

    Science.gov (United States)

    Liu, Wenxin; Venayagamoorthy, Ganesh K; Wunsch, Donald C

    2003-01-01

    Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the neuro-controller parameters. The proposed method has the features of a simple structure, adaptivity and fast response. The proposed IDNC is evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate its effectiveness and robustness.

  16. 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 ...

  17. 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.

  18. 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.

  19. Adaptive evolutionary artificial neural networks for pattern classification.

    Science.gov (United States)

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

  20. 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...... or particle filtering, generalised cross-correlation or steered response power techniques. The authors have previously developed a biologically-inspired mechanism that utilises two microphones to reactively track an acoustic signal in motion. The mechanism leverages the directional response of an mathematical...... has previously been validated in simulation and via robotic trials to track a continuous pure tone acoustic signal with a semi-circular motion trajectory and a constant but unknown angular velocity. The neural machinery has been shown to be able to learn different target angular velocities...

  1. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.

    1997-01-01

    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.

  2. 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.

  3. Verification and Validation Methodology of Real-Time Adaptive Neural Networks for Aerospace Applications

    Science.gov (United States)

    Gupta, Pramod; Loparo, Kenneth; Mackall, Dale; Schumann, Johann; Soares, Fola

    2004-01-01

    Recent research has shown that adaptive neural based control systems are very effective in restoring stability and control of an aircraft in the presence of damage or failures. The application of an adaptive neural network with a flight critical control system requires a thorough and proven process to ensure safe and proper flight operation. Unique testing tools have been developed as part of a process to perform verification and validation (V&V) of real time adaptive neural networks used in recent adaptive flight control system, to evaluate the performance of the on line trained neural networks. The tools will help in certification from FAA and will help in the successful deployment of neural network based adaptive controllers in safety-critical applications. The process to perform verification and validation is evaluated against a typical neural adaptive controller and the results are discussed.

  4. 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.

  5. 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.

  6. 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.

  7. Shaping embodied neural networks for adaptive goal-directed behavior.

    Directory of Open Access Journals (Sweden)

    Zenas C Chao

    2008-03-01

    Full Text Available The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.

  8. Real-time Adaptive Control Using Neural Generalized Predictive Control

    Science.gov (United States)

    Haley, Pam; Soloway, Don; Gold, Brian

    1999-01-01

    The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

  9. 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)

  10. 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.

  11. Neural Gas Clustering Adapted for Given Size of Clusters

    Directory of Open Access Journals (Sweden)

    Iveta Dirgová Luptáková

    2016-01-01

    Full Text Available Clustering algorithms belong to major topics in big data analysis. Their main goal is to separate an unlabelled dataset into several subsets, with each subset ideally characterized by some unique characteristic of its data structure. Common clustering approaches cannot impose constraints on sizes of clusters. However, in many applications, sizes of clusters are bounded or known in advance. One of the more recent robust clustering algorithms is called neural gas which is popular, for example, for data compression and vector quantization used in speech recognition and signal processing. In this paper, we have introduced an adapted neural gas algorithm able to accommodate requirements for the size of clusters. The convergence of algorithm towards an optimum is tested on simple illustrative examples. The proposed algorithm provides better statistical results than its direct counterpart, balanced k-means algorithm, and, moreover, unlike the balanced k-means, the quality of results of our proposed algorithm can be straightforwardly controlled by user defined parameters.

  12. 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

  13. 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

  14. 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...

  15. 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

  16. 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.

  17. Deblurring adaptive optics retinal images using deep convolutional neural networks.

    Science.gov (United States)

    Fei, Xiao; Zhao, Junlei; Zhao, Haoxin; Yun, Dai; Zhang, Yudong

    2017-12-01

    The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.

  18. 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.

  19. Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

    OpenAIRE

    Manjula Devi, R.; Kuppuswami, S.; Suganthe, R. C.

    2013-01-01

    Artificial neural network has been extensively consumed training model for solving pattern recognition tasks. However, training a very huge training data set using complex neural network necessitates excessively high training time. In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted. The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do ...

  20. Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2013-05-01

    Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.

  1. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints.

    Science.gov (United States)

    Chen, Ziting; Li, Zhijun; Chen, C L Philip

    2017-06-01

    An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.

  2. Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft

    Science.gov (United States)

    Burken, John J.

    2005-01-01

    This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example

  3. 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.

  4. Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

    OpenAIRE

    Gai, Wendong; Wang, Honglun; Zhang, Jing; Li, Yuxia

    2013-01-01

    An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed meth...

  5. Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

    OpenAIRE

    Wu, Zhonghua; Lu, Jingchao; Rajput, Jahanzeb; Shi, Jingping; Ma, Wen

    2015-01-01

    This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight ...

  6. Wavelet transform for real-time detection of action potentials in neural signals

    Directory of Open Access Journals (Sweden)

    Adam eQuotb

    2011-07-01

    Full Text Available We present a study on wavelet detection methods of neuronal Action Potentials (APs. Our final goal is to implement the selected algorithms on custom integrated electronics for on-line processing of neural signals; therefore we take real-time computing as a hard specification and silicon area as a price to pay. Using simulated neural signals including APs, we characterize an efficient wavelet method for action potential extraction by evaluating its detection rate and its implementation cost. We compare software implementation for three methods: adaptive threshold, Discrete Wavelet Transform (DWT and Stationary Wavelet Transform (SWT. We evaluate detection rate and implementation cost for detection functions dynamically comparing a signal with an adaptive threshold proportional to its standard deviation, where the signal is the raw neural signal, respectively: i non processed; ii processed by a Discrete Wavelet Transform (DWT; iii processed by a Stationary Wavelet Transform (SWT. We also use different mother wavelets and test different data formats to set an optimal compromise between accuracy and silicon cost. Detection accuracy is evaluated together with false negative and false positive detections. Simulation results show that for on-line AP detection implemented on a configurable digital integrated circuit, APs underneath the noise level can be detected using SWT with a well-selected mother wavelet, combined to an adaptive threshold.

  7. 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.

  8. Adaptive artificial neural network for autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  9. An Adaptive-PSO-Based Self-Organizing RBF Neural Network.

    Science.gov (United States)

    Han, Hong-Gui; Lu, Wei; Hou, Ying; Qiao, Jun-Fei

    2018-01-01

    In this paper, a self-organizing radial basis function (SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the network size and the parameters of an RBF neural network simultaneously. As a result, the proposed APSO-SORBF neural network can effectively generate a network model with a compact structure and high accuracy. Moreover, the analysis of convergence is given to guarantee the successful application of the APSO-SORBF neural network. Finally, multiple numerical examples are presented to illustrate the effectiveness of the proposed APSO-SORBF neural network. The results demonstrate that the proposed method is more competitive in solving nonlinear problems than some other existing SORBF neural networks.

  10. Recurrent Neural Network Identification and Adaptive Neural Control of Hydrocarbon Biodegradation Processes

    OpenAIRE

    Baruch, Ieroham; Mariaca-Gaspar, Carlos; Barrera-Cortes, Josefina

    2008-01-01

    The chapter proposes a new Kalman filter closed loop topology of recurrent neural network for identification and modeling of an unknown hydrocarbon degradation process carried out in a biopile system and a rotating drum. The proposed KF RNN contained a recurrent neural plant model, a recurrent neural output plant filter and posses global and local feedbacks. The learning algorithm is a modified version of the dynamic Backpropagation one derived using the adjoint KF RNN topology by means of th...

  11. 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.

  12. Neural recording front-end IC using action potential detection and analog buffer with digital delay for data compression.

    Science.gov (United States)

    Liu, Lei; Yao, Lei; Zou, Xiaodan; Goh, Wang Ling; Je, Minkyu

    2013-01-01

    This paper presents a neural recording analog front-end IC intended for simultaneous neural recording with action potential (AP) detection for data compression in wireless multichannel neural implants. The proposed neural recording front-end IC detects the neural spikes and sends only the preserved AP information for wireless transmission in order to reduce the overall power consumption of the neural implant. The IC consists of a low-noise neural amplifier, an AP detection circuit and an analog buffer with digital delay. The neural amplifier makes use of a current-reuse technique to maximize the transconductance efficiency for attaining a good noise efficiency factor. The AP detection circuit uses an adaptive threshold voltage to generate an enable signal for the subsequent functional blocks. The analog buffer with digital delay is employed using a finite impulse response (FIR) filter which preserves the AP waveform before the enable signal as well as provides low-pass filtering. The neural recording front-end IC has been designed using standard CMOS 0.18-µm technology occupying a core area of 220 µm by 820 µm.

  13. 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.

  14. 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.

  15. 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.

  16. Signal Processing, Pattern Formation and Adaptation in Neural Oscillators

    Science.gov (United States)

    2016-11-29

    properties that are appropriate to model networks of spiking neurons (e.g., Hodgkin & Huxley , 1952) or mean-field descriptions of neural populations...for the forced Kuramoto model. Chaos, 18(4), 043128. doi:papers2://publication/doi/10.1063/1.3049136 Eguìluz, V. M., Ospeck, M., Choe, Y ., Hudspeth

  17. Dynamic recurrent neural networks for stable adaptive control of wing rock motion

    Science.gov (United States)

    Kooi, Steven Boon-Lam

    Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model. The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

    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...

  4. 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

  5. Rapid adaptive remote focusing microscope for sensing of volumetric neural activity.

    Science.gov (United States)

    Žurauskas, Mantas; Barnstedt, Oliver; Frade-Rodriguez, Maria; Waddell, Scott; Booth, Martin J

    2017-10-01

    The ability to record neural activity in the brain of a living organism at cellular resolution is of great importance for defining the neural circuit mechanisms that direct behavior. Here we present an adaptive two-photon microscope optimized for extraction of neural signals over volumes in intact Drosophila brains, even in the presence of specimen motion. High speed volume imaging was made possible through reduction of spatial resolution while maintaining the light collection efficiency of a high resolution, high numerical aperture microscope. This enabled simultaneous recording of odor-evoked calcium transients in a defined volume of mushroom body Kenyon cell bodies in a live fruit fly.

  6. 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.

  7. 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

  8. 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

  9. 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.

  10. Parallel and interrelated neural systems underlying adaptive navigation.

    Science.gov (United States)

    Mizumori, Sheri J Y; Canfield, James G; Yeshenko, Oksana

    2005-06-01

    The ability to process in parallel multiple forms of sensory information, and link sensory-sensory associations to behavior, presumably allows for the opportunistic use of the most reliable and predictive sensory modalities in diverse behavioral contexts. Evolutionary considerations indicate that such processing may represent a fundamental operating principle underlying complex sensory associations and sensory-motor integration. Here, we suggest that animal navigation is a particularly useful model of such opportunistic use of sensory and motor information because it is possible to study directly the effects of memory on neural system functions. First, comparative evidence for parallel processing across multiple brain structures during navigation is provided from the literatures on fish and rodent navigation. Then, based on neurophysiological evidence of coordinated, multiregional processing, we provide a neurobiological explanation of learning and memory effects on neural circuitry mediating navigation.

  11. Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

    Directory of Open Access Journals (Sweden)

    Wendong Gai

    2013-01-01

    Full Text Available An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.

  12. 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

  13. Neural regulation of innate and adaptive immunity in the gut

    NARCIS (Netherlands)

    Dhawan, S.

    2017-01-01

    This thesis investigates the role of neurotransmitters acetylcholine (ACh) and norepinephrine (NE), in modulating the innate and adaptive immune function in the intestine, during physiological and pathophysiological conditions. Furthermore, this thesis attempts to advance our current understanding

  14. The Adaptive Neural Network Control of Quadrotor Helicopter

    Directory of Open Access Journals (Sweden)

    A. S. Yushenko

    2017-01-01

    Full Text Available The current steady-rising interest in using the unmanned multi-rotor aerial vehicles (UMAV designed to solve a wide range of tasks is, mainly, due to their simple design and high weight-carrying capacity as compared to classical helicopter options. Unfortunately, to solve a problem of multi-copter control is complicated because of essential nonlinearity and environmental perturbations. The most widely spread PID controllers and linear-quadratic regulators do not quite well cope with this task. The need arises for the prompt adjustment of PID controller coefficients in the course of operation or their complete re-tuning in cases of changing parameters of the control object.One of the control methods under changing conditions is the use of the sliding mode. This technology enables us to reach the stabilization and proper operation of the controlled system even under accidental external exposures and when there is a lack of the reasonably accurate mathematical model of the control object. The sliding principle is to ensure the system motion in the immediate vicinity of the sliding surface in the phase space. On the other hand, the sliding mode has some essential disadvantages. The most significant one is the high-frequency jitter of the system near the sliding surface. The sliding mode also implies the complete knowledge of the system dynamics. Various methods have been proposed to eliminate these drawbacks. For example, A.G. Aissaoui’s, H. Abid’s and M. Abid’s paper describes the application of fuzzy logic to control a drive and in Lon-Chen Hung’s and Hung-Yuan Chung’s paper an artificial neural network is used for the manipulator control.This paper presents a method of the quad-copter control with the aid of a neural network controller. This method enables us to control the system without a priori information on parameters of the dynamic model of the controlled object. The main neural network is a MIMO (“Multiple Input Multiple

  15. 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.

  16. GAUSSIAN MIXTURE MODELS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS IN AUTOMATIC SPEECH RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Natalia A. Tomashenko

    2016-11-01

    Full Text Available Subject of Research. We study speaker adaptation of deep neural network (DNN acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particular speaker. Method. A novel method for training and adaptation of deep neural network acoustic models has been developed. It is based on using an auxiliary GMM (Gaussian Mixture Models model and GMMD (GMM-derived features. The principle advantage of the proposed GMMD features is the possibility of performing the adaptation of a DNN through the adaptation of the auxiliary GMM. In the proposed approach any methods for the adaptation of the auxiliary GMM can be used, hence, it provides a universal method for transferring adaptation algorithms developed for GMMs to DNN adaptation.Main Results. The effectiveness of the proposed approach was shown by means of one of the most common adaptation algorithms for GMM models – MAP (Maximum A Posteriori adaptation. Different ways of integration of the proposed approach into state-of-the-art DNN architecture have been proposed and explored. Analysis of choosing the type of the auxiliary GMM model is given. Experimental results on the TED-LIUM corpus demonstrate that, in an unsupervised adaptation mode, the proposed adaptation technique can provide, approximately, a 11–18% relative word error reduction (WER on different adaptation sets, compared to the speaker-independent DNN system built on conventional features, and a 3–6% relative WER reduction compared to the SAT-DNN trained on fMLLR adapted features.

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

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    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...... model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...

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

    DEFF Research Database (Denmark)

    Jouffroy, Guillaume; Jouffroy, Jerome

    model, etc.) might be too complex to study. In this paper, we use a comparatively simple mechanical system, the nonholonomic vehicle referred to as the Roller-Racer, as a means towards testing different learning strategies for an Recurrent Neural Network-based (RNN) controller/guidance system. After...... 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....

  19. Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

    Science.gov (United States)

    Gao, Hui; Song, Yongduan; Wen, Changyun

    In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

  20. Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays

    Directory of Open Access Journals (Sweden)

    Weiyuan Ma

    2014-12-01

    Full Text Available In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented.

  1. 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.

  2. Neural tuning for face wholes and parts in human fusiform gyrus revealed by FMRI adaptation.

    Science.gov (United States)

    Harris, Alison; Aguirre, Geoffrey Karl

    2010-07-01

    Although the right fusiform face area (FFA) is often linked to holistic processing, new data suggest this region also encodes part-based face representations. We examined this question by assessing the metric of neural similarity for faces using a continuous carryover functional MRI (fMRI) design. Using faces varying along dimensions of eye and mouth identity, we tested whether these axes are coded independently by separate part-tuned neural populations or conjointly by a single population of holistically tuned neurons. Consistent with prior results, we found a subadditive adaptation response in the right FFA, as predicted for holistic processing. However, when holistic processing was disrupted by misaligning the halves of the face, the right FFA continued to show significant adaptation, but in an additive pattern indicative of part-based neural tuning. Thus this region seems to contain neural populations capable of representing both individual parts and their integration into a face gestalt. A third experiment, which varied the asymmetry of changes in the eye and mouth identity dimensions, also showed part-based tuning from the right FFA. In contrast to the right FFA, the left FFA consistently showed a part-based pattern of neural tuning across all experiments. Together, these data support the existence of both part-based and holistic neural tuning within the right FFA, further suggesting that such tuning is surprisingly flexible and dynamic.

  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. 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.

  5. 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...

  6. 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.

  7. Study on adaptive BTT reentry speed depletion guidance law based on BP neural network

    Science.gov (United States)

    Zheng, Zongzhun; Wang, Yongji; Wu, Hao

    2007-11-01

    Reentry guidance is one of the key technologies in hypersonic vehicle research field. In addition to the constraints on its final position coordinates, the vehicle must also impact the target from a specified direction with high precision. And therefore the adaptability of guidance law is critical to control the velocity of hypersonic vehicle and firing accuracy properly in different surroundings of large airspace. In this paper, a new adaptive guidance strategy based on Back Propagation (BP) neural network for the reentry mission of a generic hypersonic vehicle is presented. Depending on the nicer self-learn ability of BP neural network, the guidance law considers the influence of biggish mis-modeling of aerodynamics, structure error and other initial disturbances on the flight capability of vehicle. Consequently, terminal position accuracy and velocity are guaranteed, while many constraints are satisfied. Numerical simulation results clearly bring out the fact that the proposed reentry guidance law based on BP neural network is rational and effective.

  8. An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces.

    Science.gov (United States)

    Chak, C K; Feng, G; Ma, J

    1998-01-01

    An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

  9. ER fluid applications to vibration control devices and an adaptive neural-net controller

    Science.gov (United States)

    Morishita, Shin; Ura, Tamaki

    1993-07-01

    Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

  10. 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.

  11. 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.

  12. 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.

  13. Neural regulation of innate and adaptive immunity in the gut

    OpenAIRE

    Dhawan, S.

    2017-01-01

    This thesis investigates the role of neurotransmitters acetylcholine (ACh) and norepinephrine (NE), in modulating the innate and adaptive immune function in the intestine, during physiological and pathophysiological conditions. Furthermore, this thesis attempts to advance our current understanding of the gut-brain immune axis, also known as the cholinergic anti-inflammatory pathway, coined largely due to the cholinergic nature of the vagus nerve.

  14. 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.

  15. Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.

    Science.gov (United States)

    Ranganathan, Mohan Krishna; Kilmartin, Liam

    2005-09-01

    Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP).

  16. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    Science.gov (United States)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  17. 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...

  18. 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.

  19. 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.

  20. Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lee

    2011-01-01

    Full Text Available This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS, neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

  1. 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.

  2. 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.

  3. Adaptive Control Using Neural Network Augmentation for a Modified F-15 Aircraft

    Science.gov (United States)

    Burken, John J.; Williams-Hayes, Peggy; Karneshige, J. T.; Stachowiak, Susan J.

    2006-01-01

    Description of the performance of a simplified dynamic inversion controller with neural network augmentation follows. Simulation studies focus on the results with and without neural network adaptation through the use of an F-15 aircraft simulator that has been modified to include canards. Simulated control law performance with a surface failure, in addition to an aerodynamic failure, is presented. The aircraft, with adaptation, attempts to minimize the inertial cross-coupling effect of the failure (a control derivative anomaly associated with a jammed control surface). The dynamic inversion controller calculates necessary surface commands to achieve desired rates. The dynamic inversion controller uses approximate short period and roll axis dynamics. The yaw axis controller is a sideslip rate command system. Methods are described to reduce the cross-coupling effect and maintain adequate tracking errors for control surface failures. The aerodynamic failure destabilizes the pitching moment due to angle of attack. The results show that control of the aircraft with the neural networks is easier (more damped) than without the neural networks. Simulation results show neural network augmentation of the controller improves performance with aerodynamic and control surface failures in terms of tracking error and cross-coupling reduction.

  4. Adaptive Smoothing in fMRI Data Processing Neural Networks

    DEFF Research Database (Denmark)

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

    2017-01-01

    Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothing. These pipelines are commonly suboptimal, given the local optimisation strategy they use, treating each step...... 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....

  5. 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.

  6. Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2015-01-01

    Full Text Available This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity.

  7. High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm.

    Science.gov (United States)

    Gastegger, Michael; Marquetand, Philipp

    2015-05-12

    Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possible to obtain empirical potentials of high accuracy combined with the computational efficiency of conventional force fields. However, as opposed to the latter, changing bonding patterns and unusual coordination geometries can be described due to the underlying flexible functional form of the NNs. One of the most promising approaches in this field is the high-dimensional neural network (HDNN) method, which is especially adapted to the prediction of molecular properties. While HDNNs have been mostly used to model solid state systems and surface interactions, we present here the first application of the HDNN approach to an organic reaction, the Claisen rearrangement of allyl vinyl ether to 4-pentenal. To construct the corresponding HDNN potential, a new training algorithm is introduced. This algorithm is termed "element-decoupled" global extended Kalman filter (ED-GEKF) and is based on the decoupled Kalman filter. Using a metadynamics trajectory computed with density functional theory as reference data, we show that the ED-GEKF exhibits superior performance - both in terms of accuracy and training speed - compared to other variants of the Kalman filter hitherto employed in HDNN training. In addition, the effect of including forces during ED-GEKF training on the resulting potentials was studied.

  8. 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.

  9. 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.

  10. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    Science.gov (United States)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  11. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  12. 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....

  13. Learning about stress: neural, endocrine and behavioral adaptations.

    Science.gov (United States)

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD).

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

    Science.gov (United States)

    Niu, Ben; Li, Lu

    2017-04-17

    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.

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

    Science.gov (United States)

    Masri Husam Fayiz, Al

    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.

  16. 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)

  17. 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.

  18. Small-aperture monovision and the Pulfrich experience: absence of neural adaptation effects.

    Directory of Open Access Journals (Sweden)

    Sotiris Plainis

    Full Text Available PURPOSE: To explore whether adaptation reduces the interocular visual latency differences and the induced Pulfrich effect caused by the anisocoria implicit in small-aperture monovision. METHODS: Anisocoric vision was simulated in two adults by wearing in the non-dominant eye for 7 successive days, while awake, an opaque soft contact lens (CL with a small, central, circular aperture. This was repeated with aperture diameters of 1.5 and 2.5 mm. Each day, monocular and binocular pattern-reversal Visual Evoked Potentials (VEP were recorded. Additionally, the Pulfrich effect was measured: the task of the subject was to state whether a a 2-deg spot appeared in front or behind the plane of a central cross when moved left-to-right or right-to-left on a display screen. The retinal illuminance of the dominant eye was varied using neutral density (ND filters to establish the ND value which eliminated the Pulfrich effect for each lens. All experiments were performed at luminance levels of 5 and 30 cd/m(2. RESULTS: Interocular differences in monocular VEP latency (at 30 cd/m(2 rose to about 12-15 ms and 20-25 ms when the CL aperture was 2.5 and 1.5 mm, respectively. The effect was more pronounced at 5 cd/m(2 (i.e. with larger natural pupils. A strong Pulfrich effect was observed under all conditions, with the effect being less striking for the 2.5 mm aperture. No neural adaptation appeared to occur: neither the interocular differences in VEP latency nor the ND value required to null the Pulfrich effect reduced over each 7-day period of anisocoric vision. CONCLUSIONS: Small-aperture monovision produced marked interocular differences in visual latency and a Pulfrich experience. These were not reduced by adaptation, perhaps because the natural pupil diameter of the dominant eye was continually changing throughout the day due to varying illumination and other factors, making adaptation difficult.

  19. Expression of chondrogenic potential of mouse trunk neural crest cells by FGF2 treatment.

    Science.gov (United States)

    Ido, Atsushi; Ito, Kazuo

    2006-02-01

    There is a significant difference between the developmental patterns of cranial and trunk neural crest cells in the amniote. Thus, whereas cranial neural crest cells generate bone and cartilage, trunk neural crest cells do not contribute to skeletal derivatives. We examined whether mouse trunk neural crest cells can undergo chondrogenesis to analyze how the difference between the developmental patterns of cranial and trunk neural crest cells arises. Our present data demonstrate that mouse trunk neural crest cells have chondrogenic potential and that fibroblast growth factor (FGF) 2 is an inducing factor for their chondrogenesis in vitro. FGF2 altered the expression patterns of Hox9 genes and Id2, a cranial neural crest cell marker. These results suggest that environmental cues may play essential roles in generating the difference between developmental patterns of cranial and trunk neural crest cells. Copyright 2005 Wiley-Liss, Inc.

  20. Dynamics of dual prism adaptation: relating novel experimental results to a minimalistic neural model.

    Directory of Open Access Journals (Sweden)

    Orlando Arévalo

    Full Text Available In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo-motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements ('dual-adaptation'. A fundamental question is how the adaptation schedule determines the acquisition speed of new skills. Given a fixed number of movements in two different environments, will dual-adaptation be faster if switches ('phase changes' between the environments occur more frequently? We investigated the dynamics of dual-adaptation under different training schedules in a virtual pointing experiment. Surprisingly, we found that acquisition speed of dual visuo-motor mappings in a pointing task is largely independent of the number of phase changes. Next, we studied the neuronal mechanisms underlying this result and other key phenomena of dual-adaptation by relating model simulations to experimental data. We propose a simple and yet biologically plausible neural model consisting of a spatial mapping from an input layer to a pointing angle which is subjected to a global gain modulation. Adaptation is performed by reinforcement learning on the model parameters. Despite its simplicity, the model provides a unifying account for a broad range of experimental data: It quantitatively reproduced the learning rates in dual-adaptation experiments for both direct effect, i.e. adaptation to prisms, and aftereffect, i.e. behavior after removal of prisms, and their independence on the number of phase changes. Several other phenomena, e.g. initial pointing errors that are far smaller than the induced optical shift, were also captured. Moreover, the underlying mechanisms, a local adaptation of a spatial mapping and a global adaptation of a gain factor, explained asymmetric spatial transfer and generalization of prism

  1. Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

    Science.gov (United States)

    Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

    2016-06-01

    Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function. Copyright © 2016 the American Physiological Society.

  2. Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

    Science.gov (United States)

    Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

    2018-01-01

    In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. 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)

  4. 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.

  5. 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%.

  6. Social Categories Shape the Neural Representation of Emotion: Evidence from a Visual Face Adaptation Task.

    Directory of Open Access Journals (Sweden)

    Marte eOtten

    2012-02-01

    Full Text Available A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the identity of a face (such as race are often described to proceed independently of processes related to the perception of cues that can vary over time (such as emotion. Using a visual face adaptation paradigm, we tested whether these behavioral interactions between emotion and race also reflect interdependent neural representation of emotion and race. We compared visual emotion aftereffects when the adapting face and ambiguous test face differed in race or not. Emotion aftereffects were much smaller in different race trials than same race trials, indicating that the neural representation of a facial expression is significantly different depending on whether the emotional face is black or white. It thus seems that invariable cues such as race interact with variable face cues such as emotion not just at a response level, but also at the level of perception and neural representation.

  7. Calibration of a portable cost-effective chemical residue detection system with adaptive neural net control

    Science.gov (United States)

    Tripp, Alan C.; Walker, James C.

    2003-07-01

    The Sensory Research Institute at the Florida State University has quantitatively characterized a chemical residue detection system with adaptive neural net data processing. Two separate configurations, "Stormy" and "Gaea", were trained by exposure to decreasing amounts of n-amyl acetate from chemical emitters randomly distributed among a collection of non-emitters. The concentration of chemical in the sampled air stream was controlled precisely. The detection threshold for "Stormy" was 1.14 ppt; that for "Gaea" was 1.9 ppt. Cycle time for sampling and chemical analysis of each sample port was on the order of seconds. Possible effects on the sensors of environmental factors such as ambient humidity, temperature, and air velocity were not considered. Besides processing individual air sample data, the neural nets can sense concentration gradients and track to chemical source. The adaptive neural nets are accessed by a voice recognition system and are capable of point testing or free-ranging search. The service life of the detectors, the neural net processors, and auxiliary packaging is approximately 8 years under normal field use. Maintenance requires a good quality kibble and an occasional romp in the park.

  8. Social categories shape the neural representation of emotion: evidence from a visual face adaptation task.

    Science.gov (United States)

    Otten, Marte; Banaji, Mahzarin R

    2012-01-01

    A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the identity of a face (such as race) are often described to proceed independently of processes related to the perception of cues that can vary over time (such as emotion). Using a visual face adaptation paradigm, we tested whether these behavioral interactions between emotion and race also reflect interdependent neural representation of emotion and race. We compared visual emotion aftereffects when the adapting face and ambiguous test face differed in race or not. Emotion aftereffects were much smaller in different race (DR) trials than same race (SR) trials, indicating that the neural representation of a facial expression is significantly different depending on whether the emotional face is black or white. It thus seems that invariable cues such as race interact with variable face cues such as emotion not just at a response level, but also at the level of perception and neural representation.

  9. Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

    OpenAIRE

    Zhu Guoqiang; Liu Jinkun

    2015-01-01

    An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1) a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2) RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3) only one parameter needs to be updated online at each design step, whi...

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

    OpenAIRE

    Franke,Rodrigo de Azevedo; Baroni,Bruno Manfredini; Rodrigues,Rodrigo; Geremia,Jeam Marcel; Lanferdini,Fábio Juner; Vaz,Marco Aurélio

    2014-01-01

    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 electromyograp...

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

    OpenAIRE

    Yanchao Yin; Hongwei Niu; Xiaobao Liu

    2017-01-01

    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 eli...

  12. Effective neural network training with adaptive learning rate based on training loss.

    Science.gov (United States)

    Takase, Tomoumi; Oyama, Satoshi; Kurihara, Masahito

    2018-02-13

    A method that uses an adaptive learning rate is presented for training neural networks. Unlike most conventional updating methods in which the learning rate gradually decreases during training, the proposed method increases or decreases the learning rate adaptively so that the training loss (the sum of cross-entropy losses for all training samples) decreases as much as possible. It thus provides a wider search range for solutions and thus a lower test error rate. The experiments with some well-known datasets to train a multilayer perceptron show that the proposed method is effective for obtaining a better test accuracy under certain conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

    Directory of Open Access Journals (Sweden)

    Shaopei Chen

    2017-01-01

    Full Text Available Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.

  14. The impact of multiple noise sources on maximally informative adaptive dynamics in neural populations

    Science.gov (United States)

    Hsu, Wei-Mien Mendy; Kastner, David; Baccus, Stephen; Sharpee, Tatyana

    Sensory neural populations are thought to be optimized to transmit information about the sensory environment either in the course of evolution in the dynamically to adapt neural responses to the current stimulus environment. Recent work has shown that when encoding a particular stimulus feature, the existence of multiple neuronal types with different thresholds increases information transmission when sensory noise drops below a certain level. This prediction across an evolutionary timescale simultaneously explains the existence of adapting and sensitizing Off retinal ganglion cells, which have high and low thresholds for spiking, respectively, as well as the absence of comparable types among On that have higher effective noise level. However, the difference in thresholds between adapting and sensitizing cells is systematically lower than the one that would yield maximal information in an environment of stationary contrast. Here we show how predictions for the optimal threshold difference change when the overall sensory noise is treated as the combination of multiple noise sources separated by nonlinear steps. Analyzing responses of adapting and sensitizing ganglion cells types we identify an additional noise source that affects adapting but not sensitizing ganglion cells. NSF CAREER Award IIS-1254123, NSF IOS-1556388 (T.O.S.); 2016 Salk Women & Science Special Award (W.M.H); NEI Grant (S.A.B.).

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

    Science.gov (United States)

    Reybrouck, Mark; Brattico, Elvira

    2015-03-23

    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.

  16. 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.

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

    Science.gov (United States)

    Reybrouck, Mark; Brattico, Elvira

    2015-01-01

    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. PMID:25807006

  18. 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.

  19. Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions.

    Science.gov (United States)

    Siniscalchi, Sabato Marco; Salerno, Valerio Mario

    2017-08-01

    Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.

  20. 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.

  1. Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

    Science.gov (United States)

    Momoh, James A.; Wang, Yanchun; Dolce, James L.

    1997-01-01

    This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

  2. 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.

  3. Engineered Gold Nanoparticles and Plant Adaptation Potential

    Science.gov (United States)

    Siddiqi, Khwaja Salahuddin; Husen, Azamal

    2016-09-01

    Use of metal nanoparticles in biological system has recently been recognised although little is known about their possible effects on plant growth and development. Nanoparticles accumulation, translocation, growth response and stress modulation in plant system is not well understood. Plants exposed to gold and gold nanoparticles have been demonstrated to exhibit both positive and negative effects. Their growth and yield vary from species to species. Cytoxicity of engineered gold nanoparticles depends on the concentration, particle size and shape. They exhibit increase in vegetative growth and yield of fruit/seed at lower concentration and decrease them at higher concentration. Studies have shown that the gold nanoparticles exposure has improved free radical scavenging potential and antioxidant enzymatic activities and alter micro RNAs expression that regulate different morphological, physiological and metabolic processes in plants. These modulations lead to improved plant growth and yields. Prior to the use of gold nanoparticles, it has been suggested that its cost may be calculated to see if it is economically feasible.

  4. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  5. MANF Promotes Differentiation and Migration of Neural Progenitor Cells with Potential Neural Regenerative Effects in Stroke

    DEFF Research Database (Denmark)

    Tseng, Kuan-Yin; Anttila, Jenni E; Khodosevich, Konstantin

    2018-01-01

    Cerebral ischemia activates endogenous reparative processes, such as increased proliferation of neural stem cells (NSCs) in the subventricular zone (SVZ) and migration of neural progenitor cells (NPCs) toward the ischemic area. However, this reparative process is limited because most of the NPCs...

  6. 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.

  7. Reactive Power based Model Reference Neural Learning Adaptive System for Speed Estimation in Sensor-less Induction Motor Drives

    Directory of Open Access Journals (Sweden)

    K Sedhuraman

    2012-12-01

    Full Text Available In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRNLAS is proposed. The model reference adaptive system (MRAS based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI. The non-linear mapping capability of a neural network (NN and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS. In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS and reactive power based (RP-MRNLAS. The reactive power- based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RPMRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP- MRNLAS technique is demonstrated

  8. 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...... of a project crisis is often difficult. Yet, to allow fast crisis response, timely identification is critical for successful crisis management. Adaptive heuristics are judgement strategies that can strive in circumstances of limited and ambiguous information. This article presents a theoretical proposition...... 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...

  9. 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......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...... 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...

  10. 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

  11. 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.

  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. Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities

    Directory of Open Access Journals (Sweden)

    Yiyong Gou

    2017-04-01

    Full Text Available A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output (MIMO aeroelastic system in the presence of wind gust, system uncertainties, and input nonlinearities consisting of input saturation and dead-zone. In regard to the input nonlinearities, the right inverse function block of the dead-zone is added before the input nonlinearities, which simplifies the input nonlinearities into an equivalent input saturation. To deal with the equivalent input saturation, an auxiliary error system is designed to compensate for the impact of the input saturation. Meanwhile, uncertainties in pitch stiffness, plunge stiffness, and pitch damping are all considered, and radial basis function neural networks (RBFNNs are applied to approximate the system uncertainties. In combination with the designed auxiliary error system and the backstepping control technique, a constrained adaptive neural network controller is designed, and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method. Finally, extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust, system uncertainties, and input nonlinearities.

  14. 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.

  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. Neural adaptation to thin and fat bodies in the fusiform body area and middle occipital gyrus: an fMRI adaptation study.

    Science.gov (United States)

    Hummel, Dennis; Rudolf, Anne K; Brandi, Marie-Luise; Untch, Karl-Heinz; Grabhorn, Ralph; Hampel, Harald; Mohr, Harald M

    2013-12-01

    Visual perception can be strongly biased due to exposure to specific stimuli in the environment, often causing neural adaptation and visual aftereffects. In this study, we investigated whether adaptation to certain body shapes biases the perception of the own body shape. Furthermore, we aimed to evoke neural adaptation to certain body shapes. Participants completed a behavioral experiment (n = 14) to rate manipulated pictures of their own bodies after adaptation to demonstratively thin or fat pictures of their own bodies. The same stimuli were used in a second experiment (n = 16) using functional magnetic resonance imaging (fMRI) adaptation. In the behavioral experiment, after adapting to a thin picture of the own body participants also judged a thinner than actual body picture to be the most realistic and vice versa, resembling a typical aftereffect. The fusiform body area (FBA) and the right middle occipital gyrus (rMOG) show neural adaptation to specific body shapes while the extrastriate body area (EBA) bilaterally does not. The rMOG cluster is highly selective for bodies and perhaps body parts. The findings of the behavioral experiment support the existence of a perceptual body shape aftereffect, resulting from a specific adaptation to thin and fat pictures of one's own body. The fMRI results imply that body shape adaptation occurs in the FBA and the rMOG. The role of the EBA in body shape processing remains unclear. The results are also discussed in the light of clinical body image disturbances. Copyright © 2012 Wiley Periodicals, Inc.

  17. 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.

  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. 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

  2. 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.

  3. Redistribution of neural phase coherence reflects establishment of feedforward map in speech motor adaptation.

    Science.gov (United States)

    Sengupta, Ranit; Nasir, Sazzad M

    2015-04-01

    Despite recent progress in our understanding of sensorimotor integration in speech learning, a comprehensive framework to investigate its neural basis is lacking at behaviorally relevant timescales. Structural and functional imaging studies in humans have helped us identify brain networks that support speech but fail to capture the precise spatiotemporal coordination within the networks that takes place during speech learning. Here we use neuronal oscillations to investigate interactions within speech motor networks in a paradigm of speech motor adaptation under altered feedback with continuous recording of EEG in which subjects adapted to the real-time auditory perturbation of a target vowel sound. As subjects adapted to the task, concurrent changes were observed in the theta-gamma phase coherence during speech planning at several distinct scalp regions that is consistent with the establishment of a feedforward map. In particular, there was an increase in coherence over the central region and a decrease over the fronto-temporal regions, revealing a redistribution of coherence over an interacting network of brain regions that could be a general feature of error-based motor learning in general. Our findings have implications for understanding the neural basis of speech motor learning and could elucidate how transient breakdown of neuronal communication within speech networks relates to speech disorders. Copyright © 2015 the American Physiological Society.

  4. 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.

  5. Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network

    Science.gov (United States)

    Sun, W. Z.; Jiang, M. Y.; Ren, L.; Dang, J.; You, T.; Yin, F.-F.

    2017-09-01

    To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time position management (RPM) device from 138 previous 4DCT scans were retrospectively used in this study. The ADMLP-NN was composed of several artificial neural networks (ANNs) which were used as weaker predictors to compose a stronger predictor. The respiratory signal was initially smoothed using a Savitzky-Golay finite impulse response smoothing filter (S-G filter). Then, several similar multi-layer perceptron neural networks (MLP-NNs) were configured to estimate future respiratory signal position from its previous positions. Finally, an adaptive boosting (Adaboost) decision algorithm was used to set weights for each MLP-NN based on the sample prediction error of each MLP-NN. Two prediction methods, MLP-NN and ADMLP-NN (MLP-NN plus adaptive boosting), were evaluated by calculating correlation coefficient and root-mean-square-error between true and predicted signals. For predicting 500 ms ahead of prediction, average correlation coefficients were improved from 0.83 (MLP-NN method) to 0.89 (ADMLP-NN method). The average of root-mean-square-error (relative unit) for 500 ms ahead of prediction using ADMLP-NN were reduced by 27.9%, compared to those using MLP-NN. The preliminary results demonstrate that the ADMLP-NN respiratory prediction method is more accurate than the MLP-NN method and can improve the respiration prediction accuracy.

  6. Adaptive nonlinear polynomial neural networks for control of boundary layer/structural interaction

    Science.gov (United States)

    Parker, B. Eugene, Jr.; Cellucci, Richard L.; Abbott, Dean W.; Barron, Roger L.; Jordan, Paul R., III; Poor, H. Vincent

    1993-01-01

    The acoustic pressures developed in a boundary layer can interact with an aircraft panel to induce significant vibration in the panel. Such vibration is undesirable due to the aerodynamic drag and structure-borne cabin noises that result. The overall objective of this work is to develop effective and practical feedback control strategies for actively reducing this flow-induced structural vibration. This report describes the results of initial evaluations using polynomial, neural network-based, feedback control to reduce flow induced vibration in aircraft panels due to turbulent boundary layer/structural interaction. Computer simulations are used to develop and analyze feedback control strategies to reduce vibration in a beam as a first step. The key differences between this work and that going on elsewhere are as follows: that turbulent and transitional boundary layers represent broadband excitation and thus present a more complex stochastic control scenario than that of narrow band (e.g., laminar boundary layer) excitation; and secondly, that the proposed controller structures are adaptive nonlinear infinite impulse response (IIR) polynomial neural network, as opposed to the traditional adaptive linear finite impulse response (FIR) filters used in most studies to date. The controllers implemented in this study achieved vibration attenuation of 27 to 60 dB depending on the type of boundary layer established by laminar, turbulent, and intermittent laminar-to-turbulent transitional flows. Application of multi-input, multi-output, adaptive, nonlinear feedback control of vibration in aircraft panels based on polynomial neural networks appears to be feasible today. Plans are outlined for Phase 2 of this study, which will include extending the theoretical investigation conducted in Phase 2 and verifying the results in a series of laboratory experiments involving both bum and plate models.

  7. Flight envelope protection of aircraft using adaptive neural network and online linearisation

    Science.gov (United States)

    Shin, Hohyun; Kim, Youdan

    2016-03-01

    Flight envelope protection algorithm is proposed to improve the safety of an aircraft. Flight envelope protection systems find the control inputs to prevent an aircraft from exceeding structure/aerodynamic limits and maximum control surface deflections. The future values of state variables are predicted using the current states and control inputs based on linearised aircraft model. To apply the envelope protection algorithm for the wide envelope of the aircraft, online linearisation is adopted. Finally, the flight envelope protection system is designed using adaptive neural network and least-squares method. Numerical simulations are conducted to verify the performance of the proposed scheme.

  8. 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.

  9. Adaptive, integrated sensor processing to compensate for drift and uncertainty: a stochastic 'neural' approach.

    Science.gov (United States)

    Tang, T B; Chen, H; Murray, A F

    2004-02-01

    An adaptive stochastic classifier based on a simple, novel neural architecture--the Continuous Restricted Boltzmann Machine (CRBM) is demonstrated. Together with sensors and signal conditioning circuits, the classifier is capable of measuring and classifying (with high accuracy) the H+ ion concentration, in the presence of both random noise and sensor drift. Training on-line, the stochastic classifier is able to overcome significant drift of real incomplete sensor data dynamically. As analogue hardware, this signal-level sensor fusion scheme is therefore suitable for real-time analysis in a miniaturised multisensor microsystem such as a Lab-in-a-Pill (LIAP).

  10. Observer-Based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle.

    Science.gov (United States)

    Chu, Zhenzhong; Zhu, Daqi; Yang, Simon X

    2017-07-01

    This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.

  11. 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...

  12. Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints.

    Science.gov (United States)

    He, Wei; Chen, Yuhao; Yin, Zhao

    2016-03-01

    This paper studies the tracking control problem for an uncertain n -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.

  13. 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.

  14. An adaptive control for a variable speed wind turbine using RBF neural network

    Directory of Open Access Journals (Sweden)

    El Mjabber E.

    2016-01-01

    Full Text Available In this work, a controller based on Radial Basis Functions (RBF for network adaptation is considered. The adaptive Neural Network (NN control approximates the nonlinear dynamics of the wind turbine based on input/output measurement and ensures smooth tracking of optimal tip speed-ratio at different wind speeds. The wind turbine system and this controller were modeled and a program to integrate the obtained coupled equations was developed under Matlab/Simulink software package. Then, performance of the controller was studied numerically. The proposed controller was found to effectively improve the control performance against large uncertainty of the wind turbine system. comparison with nonlinear dynamic State feedback control with Kalman filter controller was performed, and the obtained results have demonstrated the relevance of this RBFNN based controller.

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

    Science.gov (United States)

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

    2017-09-01

    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.

  16. 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

  17. 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.

  18. 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.

  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. An alternative approach for adaptive real-time control using a nonparametric neural network

    Energy Technology Data Exchange (ETDEWEB)

    Alves da Silva, A.P.; Nascimento, P.C.; Lambert-Torres, G.; Borges da Silva, L.E. [Escola Federal de Engenharia de Itajuba, Minas Gerais (Brazil)

    1995-12-31

    This paper presents a nonparametric Artificial Neural Network (ANN) model for adaptive control of nonlinear systems. The proposed ANN, Functional Polynomial Network (FPN), mixes the concept of orthogonal basis functions with the idea of polynomial networks. A combination of orthogonal functions can be used to produce a desired mapping. However, there is no way besides trial and error to choose which orthogonal functions should be selected. Polynomial nets can be used for function approximation, but, it is not easy to set the order of the activation function. The combination of the two concepts produces a very powerful ANN model due to the automatic input selection capability of the polynomial networks. The proposed FPN has been tested for speed control of a DC motor. The results have been compared with the ones provided by an indirect adaptive control scheme based on multilayer perceptrons trained by backpropagation.

  1. 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)

  2. 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.

  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. Stable neural-network-based adaptive control for sampled-data nonlinear systems.

    Science.gov (United States)

    Sun, F; Sun, Z; Woo, P Y

    1998-01-01

    For a class of multiinput-multioutput (MIMO) sampled-data nonlinear systems with unknown dynamic nonlinearities, a stable neural-network (NN)-based adaptive control approach which is an integration of an NN approach and the adaptive implementation of the variable structure control with a sector, is developed. The sampled-data nonlinear system is assumed to be controllable and its state vector is available for measurement. The variable structure control with a sector serves two purposes. One is to force the system state to be within the state region in which the NN's are used when the system goes out of neural control; and the other is to provide an additional control until the system tracking error metric is controlled inside the sector within the network approximation region. The proof of a complete stability and a tracking error convergence is given and the setting of the sector and the NN parameters is discussed. It is demonstrated that the asymptotic error of the system can be made dependent only on inherent network approximation errors and the frequency range of unmodeled dynamics. Simulation studies of a two-link manipulator show the effectiveness of the proposed control approach.

  5. 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.

  6. Motor learning and cross-limb transfer rely upon distinct neural adaptation processes

    Science.gov (United States)

    Carroll, Timothy J.; Summers, Jeffery J.; Hinder, Mark R.

    2016-01-01

    Performance benefits conferred in the untrained limb after unilateral motor practice are termed cross-limb transfer. Although the effect is robust, the neural mechanisms remain incompletely understood. In this study we used noninvasive brain stimulation to reveal that the neural adaptations that mediate motor learning in the trained limb are distinct from those that underlie cross-limb transfer to the opposite limb. Thirty-six participants practiced a ballistic motor task with their right index finger (150 trials), followed by intermittent theta-burst stimulation (iTBS) applied to the trained (contralateral) primary motor cortex (cM1 group), the untrained (ipsilateral) M1 (iM1 group), or the vertex (sham group). After stimulation, another 150 training trials were undertaken. Motor performance and corticospinal excitability were assessed before motor training, pre- and post-iTBS, and after the second training bout. For all groups, training significantly increased performance and excitability of the trained hand, and performance, but not excitability, of the untrained hand, indicating transfer at the level of task performance. The typical facilitatory effect of iTBS on MEPs was reversed for cM1, suggesting homeostatic metaplasticity, and prior performance gains in the trained hand were degraded, suggesting that iTBS interfered with learning. In stark contrast, iM1 iTBS facilitated both performance and excitability for the untrained hand. Importantly, the effects of cM1 and iM1 iTBS on behavior were exclusive to the hand contralateral to stimulation, suggesting that adaptations within the untrained M1 contribute to cross-limb transfer. However, the neural processes that mediate learning in the trained hemisphere vs. transfer in the untrained hemisphere appear distinct. PMID:27169508

  7. On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

    International Nuclear Information System (INIS)

    Isaksson, Marcus; Jalden, Joakim; Murphy, Martin J.

    2005-01-01

    In this study we address the problem of predicting the position of a moving lung tumor during respiration on the basis of external breathing signals--a technique used for beam gating, tracking, and other dynamic motion management techniques in radiation therapy. We demonstrate the use of neural network filters to correlate tumor position with external surrogate markers while simultaneously predicting the motion ahead in time, for situations in which neither the breathing pattern nor the correlation between moving anatomical elements is constant in time. One pancreatic cancer patient and two lung cancer patients with mid/upper lobe tumors were fluoroscopically imaged to observe tumor motion synchronously with the movement of external chest markers during free breathing. The external marker position was provided as input to a feed-forward neural network that correlated the marker and tumor movement to predict the tumor position up to 800 ms in advance. The predicted tumor position was compared to its observed position to establish the accuracy with which the filter could dynamically track tumor motion under nonstationary conditions. These results were compared to simplified linear versions of the filter. The two lung cancer patients exhibited complex respiratory behavior in which the correlation between surrogate marker and tumor position changed with each cycle of breathing. By automatically and continuously adjusting its parameters to the observations, the neural network achieved better tracking accuracy than the fixed and adaptive linear filters. Variability and instability in human respiration complicate the task of predicting tumor position from surrogate breathing signals. Our results show that adaptive signal-processing filters can provide more accurate tumor position estimates than simpler stationary filters when presented with nonstationary breathing motion

  8. Multivariate adaptive regression splines and neural network models for prediction of pile drivability

    Directory of Open Access Journals (Sweden)

    Wengang Zhang

    2016-01-01

    Full Text Available Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved. In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system's predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS, as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network (BPNN and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses (MCS, Maximum tensile stresses (MTS, and Blow per foot (BPF. A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.

  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. The potential transformation of our species by neural enhancement.

    Science.gov (United States)

    Zehr, E Paul

    2015-01-01

    Neural enhancement represents recovery of function that has been lost due to injury or disease pathology. Restoration of functional ability is the objective. For example, a neuroprosthetic to replace a forearm and hand lost to the ravages of war or industrial accident. However, the same basic constructs used for neural enhancement after injury could amplify abilities that are already in the natural normal range. That is, neural enhancement technologies to restore function and improve daily abilities for independent living could be used to improve so-called normal function to ultimate function. Approaching that functional level by use and integration of technology takes us toward the concept of a new species. This new subspecies--homo sapiens technologicus--is one that uses technology not just to assist but to change its own inherent biological function. The author uses examples from prosthetics and neuroprosthetics to address the issue of the limitations of constructs on the accepted range of human performance ability and aims to provide a cautionary view toward reflection on where our science may take the entire species.

  12. 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

  13. The neural dynamics of somatosensory processing and adaptation across childhood: a high-density electrical mapping study

    Science.gov (United States)

    Uppal, Neha; Foxe, John J.; Butler, John S.; Acluche, Frantzy

    2016-01-01

    Young children are often hyperreactive to somatosensory inputs hardly noticed by adults, as exemplified by irritation to seams or labels in clothing. The neurodevelopmental mechanisms underlying changes in sensory reactivity are not well understood. Based on the idea that neurodevelopmental changes in somatosensory processing and/or changes in sensory adaptation might underlie developmental differences in somatosensory reactivity, high-density electroencephalography was used to examine how the nervous system responds and adapts to repeated vibrotactile stimulation over childhood. Participants aged 6–18 yr old were presented with 50-ms vibrotactile stimuli to the right wrist over the median nerve at 5 blocked interstimulus intervals (ranging from ∼7 to ∼1 stimulus per second). Somatosensory evoked potentials (SEPs) revealed three major phases of activation within the first 200 ms, with scalp topographies suggestive of neural generators in contralateral somatosensory cortex. Although overall SEPs were highly similar for younger, middle, and older age groups (6.1–9.8, 10.0–12.9, and 13.0–17.8 yr old), there were significant age-related amplitude differences in initial and later phases of the SEP. In contrast, robust adaptation effects for fast vs. slow presentation rates were observed that did not differ as a function of age. A greater amplitude response in the later portion of the SEP was observed for the youngest group and may be related to developmental changes in responsivity to somatosensory stimuli. These data suggest the protracted development of the somatosensory system over childhood, whereas adaptation, as assayed in this study, is largely in place by ∼7 yr of age. PMID:26763781

  14. The neural dynamics of somatosensory processing and adaptation across childhood: a high-density electrical mapping study.

    Science.gov (United States)

    Uppal, Neha; Foxe, John J; Butler, John S; Acluche, Frantzy; Molholm, Sophie

    2016-03-01

    Young children are often hyperreactive to somatosensory inputs hardly noticed by adults, as exemplified by irritation to seams or labels in clothing. The neurodevelopmental mechanisms underlying changes in sensory reactivity are not well understood. Based on the idea that neurodevelopmental changes in somatosensory processing and/or changes in sensory adaptation might underlie developmental differences in somatosensory reactivity, high-density electroencephalography was used to examine how the nervous system responds and adapts to repeated vibrotactile stimulation over childhood. Participants aged 6-18 yr old were presented with 50-ms vibrotactile stimuli to the right wrist over the median nerve at 5 blocked interstimulus intervals (ranging from ∼7 to ∼1 stimulus per second). Somatosensory evoked potentials (SEPs) revealed three major phases of activation within the first 200 ms, with scalp topographies suggestive of neural generators in contralateral somatosensory cortex. Although overall SEPs were highly similar for younger, middle, and older age groups (6.1-9.8, 10.0-12.9, and 13.0-17.8 yr old), there were significant age-related amplitude differences in initial and later phases of the SEP. In contrast, robust adaptation effects for fast vs. slow presentation rates were observed that did not differ as a function of age. A greater amplitude response in the later portion of the SEP was observed for the youngest group and may be related to developmental changes in responsivity to somatosensory stimuli. These data suggest the protracted development of the somatosensory system over childhood, whereas adaptation, as assayed in this study, is largely in place by ∼7 yr of age. Copyright © 2016 the American Physiological Society.

  15. Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks.

    Science.gov (United States)

    Savran, Aydogan; Tasaltin, Ramazan; Becerikli, Yasar

    2006-04-01

    This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control

  16. 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.

  17. 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.

  18. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    Science.gov (United States)

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel L

    2017-03-01

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNN-based and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p <;0.001, Wilcoxon).

  19. 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

  20. Neural Network-Based Adaptive Backstepping Control for Hypersonic Flight Vehicles with Prescribed Tracking Performance

    Directory of Open Access Journals (Sweden)

    Zhu Guoqiang

    2015-01-01

    Full Text Available An adaptive neural control scheme is proposed for a class of generic hypersonic flight vehicles. The main advantages of the proposed scheme include the following: (1 a new constraint variable is defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries; (2 RBF NNs are employed to compensate for complex and uncertain terms to solve the problem of controller complexity; (3 only one parameter needs to be updated online at each design step, which significantly reduces the computational burden. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

  1. A Tool for Verification and Validation of Neural Network Based Adaptive Controllers for High Assurance Systems

    Science.gov (United States)

    Gupta, Pramod; Schumann, Johann

    2004-01-01

    High reliability of mission- and safety-critical software systems has been identified by NASA as a high-priority technology challenge. We present an approach for the performance analysis of a neural network (NN) in an advanced adaptive control system. This problem is important in the context of safety-critical applications that require certification, such as flight software in aircraft. We have developed a tool to measure the performance of the NN during operation by calculating a confidence interval (error bar) around the NN's output. Our tool can be used during pre-deployment verification as well as monitoring the network performance during operation. The tool has been implemented in Simulink and simulation results on a F-15 aircraft are presented.

  2. 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.

  3. 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)

  4. A simplified adaptive neural network prescribed performance controller for uncertain MIMO feedback linearizable systems.

    Science.gov (United States)

    Theodorakopoulos, Achilles; Rovithakis, George A

    2015-03-01

    In this paper, the problem of deriving a continuous, state-feedback controller for a class of multiinput multioutput feedback linearizable systems is considered with special emphasis on controller simplification and reduction of the overall design complexity with respect to the current state of the art. The proposed scheme achieves prescribed bounds on the transient and steady-state performance of the output tracking errors despite the uncertainty in system nonlinearities. Contrary to the current state of the art, however, only a single neural network is utilized to approximate a scalar function that partly incorporates the system nonlinearities. Furthermore, the loss of model controllability problem, typically introduced owing to approximation model singularities, is avoided without attaching additional complexity to the control or adaptive law. Simulations are performed to verify and clarify the theoretical findings.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Adaptive neural control of nonlinear MIMO systems with time-varying output constraints.

    Science.gov (United States)

    Meng, Wenchao; Yang, Qinmin; Sun, Youxian

    2015-05-01

    In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control input's bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

  10. Neural correlates of the age-related changes in motor sequence learning and motor adaptation in older adults.

    Science.gov (United States)

    King, Bradley R; Fogel, Stuart M; Albouy, Geneviève; Doyon, Julien

    2013-01-01

    As the world's population ages, a deeper understanding of the relationship between aging and motor learning will become increasingly relevant in basic research and applied settings. In this context, this review aims to address the effects of age on motor sequence learning (MSL) and motor adaptation (MA) with respect to behavioral, neurological, and neuroimaging findings. Previous behavioral research investigating the influence of aging on motor learning has consistently reported the following results. First, the initial acquisition of motor sequences is not altered, except under conditions of increased task complexity. Second, older adults demonstrate deficits in motor sequence memory consolidation. And, third, although older adults demonstrate deficits during the exposure phase of MA paradigms, the aftereffects following removal of the sensorimotor perturbation are similar to young adults, suggesting that the adaptive ability of older adults is relatively intact. This paper will review the potential neural underpinnings of these behavioral results, with a particular emphasis on the influence of age-related dysfunctions in the cortico-striatal system on motor learning.

  11. Neural correlates of the age-related changes in motor sequence learning and motor adaptation in older adults

    Directory of Open Access Journals (Sweden)

    Bradley R King

    2013-04-01

    Full Text Available As the world’s population ages, a deeper understanding of the relationship between aging and motor learning will become increasingly relevant in basic research and applied settings. In this context, this review aims to address the effects of age on motor sequence learning (MSL and motor adaptation (MA with respect to behavioral, neurological and neuroimaging findings. Previous behavioral research investigating the influence of aging on motor learning has consistently reported the following results. First, the initial acquisition of motor sequences is not altered, except under conditions of increased task complexity. Second, older adults demonstrate deficits in motor sequence memory consolidation. And, third, although older adults demonstrate deficits during the exposure phase of MA paradigms, the aftereffects following removal of the sensorimotor perturbation are similar to young adults, suggesting that the adaptive ability of older adults is relatively intact. This paper will review the potential neural underpinnings of these behavioral results, with a particular emphasis on the influence of age-related dysfunctions in the cortico-striatal system on motor learning.

  12. 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

  13. 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

  14. Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time.

    Science.gov (United States)

    Ge, Shuzhi Sam; Zhang, Jin; Lee, Tong Heng

    2004-08-01

    In this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme.

  15. Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

    Directory of Open Access Journals (Sweden)

    Huanqing Wang

    2014-01-01

    Full Text Available The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points. It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

  16. Learning vector quantization neural network–based model reference adaptive control method for intelligent lower-limb prosthesis

    Directory of Open Access Journals (Sweden)

    Jia-Qiang Yang

    2016-04-01

    Full Text Available This article focuses on the design of a control system for intelligent prostheses. Learning vector quantization neural network–based model reference adaptive control method is employed to implement real-time trajectory tracking and damp torque control of intelligent lower-limb prosthesis. The method is then analyzed and proposed. A model reference control system is first built with two learning vector quantization neural networks. One neural network is used for output prediction, and the other is used for input control. The angle information of the prosthetic knee joint is utilized to train these two neural networks with the given learning algorithm. The testing results of different movement patterns verify the effectiveness of the proposed method and its suitability for intelligent lower-limb prostheses.

  17. An innovative Neural-Fuzzy adaptive Kalman filter for ultra-tightly coupled GPS/INS integrated system

    Science.gov (United States)

    Wu, Jiabao

    2017-10-01

    Considering all integration methods of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system, ultra-tightly coupled method is with no doubt the best because the mutual assistance is further enhanced and navigation performance is obviously improved. However, UTC GPS/INS system is still affected by changing noise of GPS signals due to the pre-defined constant measurement noise model. To solve this problem a neural-fuzzy adaptive Kalman filter for UTC GPS/INS system is proposed. Fuzzy adaptive controller adjusts the measurement noise model online according to the innovation sequence provided by the Integration Kalman Filter (IKF). Since the design of the fuzzy logic controller is very empirical, a neural network (NN) is developed to achieve the parameter optimization for the fuzzy logic controller. To prove that the innovative neural-fuzzy adaptive IKF is efficient, a simulation package which includes all procedures of UTC GPS/INS system is employed and results are explained in detail. In conclusion, neural-fuzzy adaptive IKF further improves the performance of the UTC GPS/INS system in noise-changing environments.

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

    DEFF Research Database (Denmark)

    Junius, D.; Dau, Torsten

    2005-01-01

    . This nonlinearity in terms of temporal processing most likely reflects effects of short-term adaptation. Overall, the results of the present study further demonstrate the importance of cochlear processing for the formation of brainstem potentials. The data may provide constraints on future models of peripheral...

  19. Adaptive Neural Networks Prescribed Performance Control Design for Switched Interconnected Uncertain Nonlinear Systems.

    Science.gov (United States)

    Li, Yongming; Tong, Shaocheng

    2017-06-28

    In this paper, an adaptive neural networks (NNs)-based decentralized control scheme with the prescribed performance is proposed for uncertain switched nonstrict-feedback interconnected nonlinear systems. It is assumed that nonlinear interconnected terms and nonlinear functions of the concerned systems are unknown, and also the switching signals are unknown and arbitrary. A linear state estimator is constructed to solve the problem of unmeasured states. The NNs are employed to approximate unknown interconnected terms and nonlinear functions. A new output feedback decentralized control scheme is developed by using the adaptive backstepping design technique. The control design problem of nonlinear interconnected switched systems with unknown switching signals can be solved by the proposed scheme, and only a tuning parameter is needed for each subsystem. The proposed scheme can ensure that all variables of the control systems are semi-globally uniformly ultimately bounded and the tracking errors converge to a small residual set with the prescribed performance bound. The effectiveness of the proposed control approach is verified by some simulation results.

  20. Neural adaptations after short-term wingate-based high-intensity interval training

    Science.gov (United States)

    Vera-Ibañez, Antonio; Colomer-Poveda, David; Romero-Arenas, Salvador; Viñuela-García, Manuel; Márquez, Gonzalo

    2017-01-01

    Objectives: This study examined the neural adaptations associated with a low-volume Wingate-based High Intensity Interval Training (HIIT). Methods: Fourteen recreationally trained males were divided into an experimental (HIIT) and a control group to determine whether a short-term (4 weeks) Wingate-based HIIT program could alter the Hoffmann (H-) reflex, volitional (V-) wave and maximum voluntary contraction (MVC) of the plantar-flexor muscles, and the peak power achieved during a Wingate test. Results: Absolute and relative peak power increased in the HIIT group (ABS_Ppeak: +14.7%, P=0.001; and REL_Ppeak: +15.0%, P=0.001), but not in the control group (ABS_Ppeak: P=0.466; and REL_Ppeak: P=0.493). However, no significant changes were found in the MVC (P>0.05 for both groups). There was a significant increase in H-reflex size after HIIT (+24.5%, P=0.004), while it remained unchanged in the control group (P=0.134). No significant changes were observed either in the V-wave or in the Vwave/Mwave ratio (P>0.05 for both groups). Conclusion: The Wingate-based training led to an increased peak power together with a higher spinal excitability. However, no changes were found either in the volitional wave or in the MVC, indicating a lack of adaptation in the central motor drive. PMID:29199186

  1. Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

    Science.gov (United States)

    Szu, Harold H.

    1990-01-01

    In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously.

  2. Electric load simulator system control based on adaptive particle swarm optimization wavelet neural network with double sliding modes

    Directory of Open Access Journals (Sweden)

    Chao Wang

    2016-08-01

    Full Text Available In this article, an adaptive particle swarm optimization wavelet neural network with double sliding modes controller is proposed to address the complex nonlinearities and uncertainties in the electric load simulator. The adaptive double sliding modes–particle swarm optimization wavelet neural network algorithm with the self-learning structures and parameters is designed as a torque tracking controller, in which a number of hidden nodes are added and pruned by the structure learning algorithm, and the parameters are online adjusted by the adaptive particle swarm optimization at the same time. Moreover, one conventional sliding mode is introduced to track the time-varying reference command, and the other complementary sliding mode is adopted to attenuate the effect of the approximation error. Furthermore, the relative parameters should comply with some estimation laws on the basis of the Lyapunov theory used to guarantee the system stability. Finally, the simulation experiments are carried out on the hardware-in-the-loop platform for the electric load simulator, the performance of the adaptive double sliding modes–particle swarm optimization wavelet neural network with structure learning is verified compared with some similar control methods. In addition, different amplitudes and frequencies of the reference commands are introduced to further evaluate the effectiveness and robustness of the proposed algorithms.

  3. An Adaptive Framework for Acoustic Monitoring of Potential Hazards

    Directory of Open Access Journals (Sweden)

    Potamitis Ilyas

    2009-01-01

    Full Text Available Robust recognition of general audio events constitutes a topic of intensive research in the signal processing community. This work presents an efficient methodology for acoustic surveillance of atypical situations which can find use under different acoustic backgrounds. The primary goal is the continuous acoustic monitoring of a scene for potentially hazardous events in order to help an authorized officer to take the appropriate actions towards preventing human loss and/or property damage. A probabilistic hierarchical scheme is designed based on Gaussian mixture models and state-of-the-art sound parameters selected through extensive experimentation. A feature of the proposed system is its model adaptation loop that provides adaptability to different sound environments. We report extensive experimental results including installation in a real environment and operational detection rates for three days of function on a 24 hour basis. Moreover, we adopt a reliable testing procedure that demonstrates high detection rates as regards average recognition, miss probability, and false alarm rates.

  4. 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.

  5. Interatomic potential construction with self-learning and adaptive database

    Science.gov (United States)

    Miwa, Kazutoshi; Ohno, Hiroshi

    2017-10-01

    We have developed an automatic machine learning potential (MLP) construction scheme, the self-learning and adaptive database (SLAD). The sample structures for training are collected by the molecular dynamics simulations using the MLP itself with the aid of the spilling factor for simultaneous error estimation. The utility of the SLAD is demonstrated by applying it for the solid-state ionic conductor, Li2B12H12 . Starting from the low-temperature α -phase structure, the MLP is successfully developed, for which the total number of density functional calculations required is only 84. The constructed MLP reproduces well the phase transition to the high-temperature β phase accompanied by an abrupt lattice expansion, promotion of Li diffusion, and orientational disordering of B12H12 complexes. The SLAD allows us the efficient construction of MLPs for complicated systems.

  6. Adaptive coded aperture imaging: progress and potential future applications

    Science.gov (United States)

    Gottesman, Stephen R.; Isser, Abraham; Gigioli, George W., Jr.

    2011-09-01

    Interest in Adaptive Coded Aperture Imaging (ACAI) continues to grow as the optical and systems engineering community becomes increasingly aware of ACAI's potential benefits in the design and performance of both imaging and non-imaging systems , such as good angular resolution (IFOV), wide distortion-free field of view (FOV), excellent image quality, and light weight construct. In this presentation we first review the accomplishments made over the past five years, then expand on previously published work to show how replacement of conventional imaging optics with coded apertures can lead to a reduction in system size and weight. We also present a trade space analysis of key design parameters of coded apertures and review potential applications as replacement for traditional imaging optics. Results will be presented, based on last year's work of our investigation into the trade space of IFOV, resolution, effective focal length, and wavelength of incident radiation for coded aperture architectures. Finally we discuss the potential application of coded apertures for replacing objective lenses of night vision goggles (NVGs).

  7. Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

    Directory of Open Access Journals (Sweden)

    Muhammad Ammirrul Atiqi Mohd Zainuri

    2016-05-01

    Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.

  8. Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training.

    Science.gov (United States)

    Narayanan, Arun; Wang, DeLiang

    2015-01-01

    Although deep neural network (DNN) acoustic models are known to be inherently noise robust, especially with matched training and testing data, the use of speech separation as a frontend and for deriving alternative feature representations has been shown to improve performance in challenging environments. We first present a supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions. The system performs separation via ratio time-frequency masking; the ideal ratio mask (IRM) is estimated using DNNs. We then propose a framework that unifies separation and acoustic modeling via joint adaptive training. Since the modules for acoustic modeling and speech separation are implemented using DNNs, unification is done by introducing additional hidden layers with fixed weights and appropriate network architecture. On the CHiME-2 medium-large vocabulary ASR task, and with log mel spectral features as input to the acoustic model, an independently trained ratio masking frontend improves word error rates by 10.9% (relative) compared to the noisy baseline. In comparison, the jointly trained system improves performance by 14.4%. We also experiment with alternative feature representations to augment the standard log mel features, like the noise and speech estimates obtained from the separation module, and the standard feature set used for IRM estimation. Our best system obtains a word error rate of 15.4% (absolute), an improvement of 4.6 percentage points over the next best result on this corpus.

  9. 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.

  10. Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

    Science.gov (United States)

    Grejner-Brzezinska, Dorota A.; Toth, Charles; Moafipoor, Shahram

    2007-11-01

    The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.

  11. 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.

  12. Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping

    Science.gov (United States)

    Xu, Bin; Sun, Fuchun; Yang, Chenguang; Gao, Daoxiang; Ren, Jianxin

    2011-09-01

    In this article, the adaptive neural controller in discrete time is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. The dynamics are decomposed into the altitude subsystem and the velocity subsystem. The altitude subsystem is transformed into the strict-feedback form from which the discrete-time model is derived by the first-order Taylor expansion. The virtual control is designed with nominal feedback and neural network (NN) approximation via back-stepping. Meanwhile, one adaptive NN controller is designed for the velocity subsystem. To avoid the circular construction problem in the practical control, the design of coefficients adopts the upper bound instead of the nominal value. Under the proposed controller, the semiglobal uniform ultimate boundedness stability is guaranteed. The square and step responses are presented in the simulation studies to show the effectiveness of the proposed control approach.

  13. 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...... 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...... conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs....

  14. 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.

  15. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    OpenAIRE

    Zhijia Chen; Yuanchang Zhu; Yanqiang Di; Shaochong Feng

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is const...

  16. Adaptive lyapunov control and artificial neural networks for spacecraft relative maneuvering using atmospheric differential drag

    Science.gov (United States)

    Perez Chaparro, David Andres

    At low Earth orbits, a differential in the drag acceleration between spacecraft can be used to control their relative motion. This drag differential allows for a propellant-free alternative to thrusters for performing relative maneuvers in these orbits. The interest in autonomous propellant-less maneuvering comes from the desire to reduce the costs of spacecraft formations. Formation maneuvering opens up a wide variety of new applications for spacecraft missions, such as on-orbit maintenance and refueling. In this work atmospheric differential drag based nonlinear controllers are presented that can be used for virtually any planar relative maneuver of two spacecraft, provided that there is enough atmospheric density and that the spacecraft can change their ballistic coefficients by sufficient amounts to generate the necessary differential accelerations. The control techniques are successfully tested using high fidelity Satellite Tool Kit simulations for re-phase, fly-around, and rendezvous maneuvers, proving the feasibility of the proposed approach for a real flight. Furthermore, the atmospheric density varies in time and in space as the spacecraft travel along their orbits. The ability to accurately forecast the density allows for accurate onboard orbit propagation and for creating realistic guidance trajectories for maneuvers that rely on the differential drag. In this work a localized density predictor based on artificial neural networks is also presented. The predictor uses density measurements or estimates along the past orbits and can use a set of proxies for solar and geomagnetic activities to predict the value of the density along the future orbits of the spacecraft. The performance of the localized predictor is studied for different neural network structures, testing periods of high and low solar and geomagnetic activities and different prediction windows. Comparison with previously developed methods show substantial benefits in using neural networks, both

  17. 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...... and weaknesses of a Nordic strategy for adaptation? In order to answer these two questions, this paper examines reviews the current national adaptation policies of each Nordic country and discusses the challenges facing a Nordic strategy and finally assesses the potential for common Nordic adaptation policy...

  18. COMPOSITE MATERIALS' CONDITION CLASSIFIER BASED ON NEURAL NETWORK OF ADAPTIVE RESONANCE THEORY

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2012-04-01

    Full Text Available In this article proposed to use a modified neural network Fuzzy-ART for classification of thetechnical condition of composite materials. This neural network is used as a part of nondestructivetesting system to perform diagnosis of composite materials and provides cluster analysis andclassification of units under test. The advantage of the described neural network and the system ingeneral is its flexible architecture, high performance and high reliability of data processing

  19. 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...

  20. USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

    Directory of Open Access Journals (Sweden)

    M. Ahmadlou

    2015-12-01

    Full Text Available 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.

  1. 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.

  2. 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

  3. Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction.

    Science.gov (United States)

    Wen, Yuntong; Ren, Xuemei

    2011-10-01

    This paper investigates a neural network (NN) state observer-based adaptive control for a class of time-varying delays nonlinear systems with unknown control direction. An adaptive neural memoryless observer, in which the knowledge of time-delay is not used, is designed to estimate the system states. Furthermore, by applying the property of the function tanh(2)(ϑ/ε)/ϑ (the function can be defined at ϑ = 0) and introducing a novel type appropriate Lyapunov-Krasovskii functional, an adaptive output feedback controller is constructed via backstepping method which can efficiently avoid the problem of controller singularity and compensate for the time-delay. It is highly proven that the closed-loop systems controller designed by the NN-basis function property, new kind parameter adaptive law and Nussbaum function in detecting the control direction is able to guarantee the semi-global uniform ultimate boundedness of all signals and the tracking error can converge to a small neighborhood of zero. The characteristic of the proposed approach is that it relaxes any restrictive assumptions of Lipschitz condition for the unknown nonlinear continuous functions. And the proposed scheme is suitable for the systems with mismatching conditions and unmeasurable states. Finally, two simulation examples are given to illustrate the effectiveness and applicability of the proposed approach. © 2011 IEEE

  4. A 41 μW real-time adaptive neural spike classifier

    NARCIS (Netherlands)

    Zjajo, A.; van Leuken, T.G.R.M.

    2016-01-01

    Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike

  5. Evidence of genomic adaptation to climate in Eucalyptus microcarpa: Implications for adaptive potential to projected climate change.

    Science.gov (United States)

    Jordan, Rebecca; Hoffmann, Ary A; Dillon, Shannon K; Prober, Suzanne M

    2017-11-01

    Understanding whether populations can adapt in situ or whether interventions are required is of key importance for biodiversity management under climate change. Landscape genomics is becoming an increasingly important and powerful tool for rapid assessments of climate adaptation, especially in long-lived species such as trees. We investigated climate adaptation in Eucalyptus microcarpa using the DArTseq genomic approach. A combination of F ST outlier and environmental association analyses were performed using >4200 genomewide single nucleotide polymorphisms (SNPs) from 26 populations spanning climate gradients in southeastern Australia. Eighty-one SNPs were identified as putatively adaptive, based on significance in F ST outlier tests and significant associations with one or more climate variables related to temperature (70/81), aridity (37/81) or precipitation (35/81). Adaptive SNPs were located on all 11 chromosomes, with no particular region associated with individual climate variables. Climate adaptation appeared to be characterized by subtle shifts in allele frequencies, with no consistent fixed differences identified. Based on these associations, we predict adaptation under projected changes in climate will include a suite of shifts in allele frequencies. Whether this can occur sufficiently rapidly through natural selection within populations, or would benefit from assisted gene migration, requires further evaluation. In some populations, the absence or predicted increases to near fixation of particular adaptive alleles hint at potential limits to adaptive capacity. Together, these results reinforce the importance of standing genetic variation at the geographic level for maintaining species' evolutionary potential. © 2017 John Wiley & Sons Ltd.

  6. Adaptive Output Neural Network Control for a Class of Stochastic Nonlinear Systems With Dead-Zone Nonlinearities.

    Science.gov (United States)

    Wu, Li-Bing; Yang, Guang-Hong

    2017-03-01

    This paper investigates the problem of adaptive output neural network (NN) control for a class of stochastic nonaffine and nonlinear systems with actuator dead-zone inputs. First, based on the intermediate value theorem, a novel design scheme that converts the nonaffine system into the corresponding affine system is developed. In particular, the priori knowledge of the bound of the derivative of the nonaffine and nonlinear functions is removed; then, by employing NNs to approximate the appropriate nonlinear functions, the corresponding adaptive NN tracking controller with the adjustable parameter updated laws is designed through a backstepping technique. Furthermore, it is shown that all the closed-loop signals are bounded in probability, and the system output tracking error can converge to a small neighborhood in the sense of a mean quartic value. Finally, experimental simulations are provided to demonstrate the efficiency of the proposed adaptive NN tracking control method.

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Jia Jin

    2015-01-01

    Full Text Available 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.

  9. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials

    OpenAIRE

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Mill?n-Calenti, Jos? Carlos; Del Percio, Claudio; Buj?n, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between “seen” trials and “not seen” trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expre...

  10. 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)

  11. 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

  12. 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

  13. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  14. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Directory of Open Access Journals (Sweden)

    Zhijia Chen

    2015-01-01

    Full Text Available In IaaS (infrastructure as a service cloud environment, users are provisioned with virtual machines (VMs. To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN. We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  15. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  16. 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

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

    OpenAIRE

    Sesina Iryna M.

    2014-01-01

    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 per...

  18. 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.

  19. Intrinsic lens potential of neural retina inhibited by Notch signaling as the cause of lens transdifferentiation.

    Science.gov (United States)

    Iida, Hideaki; Ishii, Yasuo; Kondoh, Hisato

    2017-01-15

    Embryonic neural retinas of avians produce lenses under spreading culture conditions. This phenomenon has been regarded as a paradigm of transdifferentiation due to the overt change in cell type. Here we elucidated the underlying mechanisms. Retina-to-lens transdifferentiation occurs in spreading cultures, suggesting that it is triggered by altered cell-cell interactions. Thus, we tested the involvement of Notch signaling based on its role in retinal neurogenesis. Starting from E8 retina, a small number of crystallin-expressing lens cells began to develop after 20 days in control spreading cultures. By contrast, addition of Notch signal inhibitors to cultures after day 2 strongly promoted lens development beginning at day 11, and a 10-fold increase in δ-crystallin expression level. After Notch signal inhibition, transcription factor genes that regulate the early stage of eye development, Prox1 and Pitx3, were sequentially activated. These observations indicate that the lens differentiation potential is intrinsic to the neural retina, and this potential is repressed by Notch signaling during normal embryogenesis. Therefore, Notch suppression leads to lens transdifferentiation by disinhibiting the neural retina-intrinsic program of lens development. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. 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.

  1. 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

  2. Consequences of converting graded to action potentials upon neural information coding and energy efficiency.

    Directory of Open Access Journals (Sweden)

    Biswa Sengupta

    2014-01-01

    Full Text Available 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.

  3. 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.

  4. Analysis of the Earth's magnetosphere states using the algorithm of adaptive construction of hierarchical neural network classifiers

    Science.gov (United States)

    Dolenko, Sergey; Svetlov, Vsevolod; Isaev, Igor; Myagkova, Irina

    2017-10-01

    This paper presents analysis of the results of clusterization of the array of increases in the flux of relativistic electrons in the outer radiation belt of the Earth by two clustering algorithms. One of them is the algorithm for adaptive construction of hierarchical neural network classifiers developed by the authors, applied in clustering mode; the other one is the well-known k-means clusterization algorithm. The obtained clusters are analysed from the point of view of their possible matching to characteristic types of events, the partitions obtained by both methods are compared with each other.

  5. 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.

  6. Quantifying the adaptive potential of an antibiotic resistance enzyme

    NARCIS (Netherlands)

    Schenk, M.F.; Szendro, I.G.; Krug, J.; Visser, de J.A.G.M.

    2012-01-01

    For a quantitative understanding of the process of adaptation, we need to understand its “raw material,” that is, the frequency and fitness effects of beneficial mutations. At present, most empirical evidence suggests an exponential distribution of fitness effects of beneficial mutations, as

  7. Exploring the potential for neural recovery after incomplete tetraplegia through nonsurgical interventions.

    Science.gov (United States)

    Backus, Deborah

    2010-12-01

    Persons with tetraplegia resulting from a cervical spinal cord injury believe that increasing upper limb (UL) function will improve their quality of life. Various lines of evidence demonstrate that persons with incomplete tetraplegia have the potential for improvements in both neural plasticity and function of the arms and hands. Therefore treatment to improve UL function in persons with incomplete tetraplegia should focus on improving motor control, not just compensation for the paralysis and sensory loss that follows a spinal cord injury. This article highlights the principles that underlie the facilitation of neural plasticity and functional changes: intensity, repeated practice, attention, and somatosensory augmentation. Evidence is presented for the application of these principles through the use of activity-based interventions in persons with tetraplegia and the hypothesis is proposed that the use of activity-based interventions will lead to greater, more beneficial neural plasticity as well as gains in UL function, in persons with incomplete tetraplegia. Copyright © 2010 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  8. Emotions and neural processing of respiratory sensations investigated with respiratory-related evoked potentials.

    Science.gov (United States)

    von Leupoldt, Andreas; Chan, Pei-Ying S; Esser, Roland W; Davenport, Paul W

    2013-04-01

    Patients with respiratory diseases such as asthma and chronic obstructive pulmonary disease frequently experience respiratory sensations, which are often perceived as unpleasant or threatening. However, the accurate perception of respiratory sensations is important for the management and treatment of these diseases. Emotions can substantially influence the perception of respiratory sensations and might affect the course of respiratory diseases, but the underlying neural mechanisms are poorly understood. The respiratory-related evoked potential (RREP) recorded from the electroencephalogram is a noninvasive technique that allowed first studies to examine the impact of emotions on the neural processing of respiratory sensations. In this review, we will briefly introduce the importance of the perception of respiratory sensations and the influence of emotions on respiratory perception. We then provide an overview on the technique of RREP and present a systematic review on recent findings using this technique in the context of emotions. The evidence currently available from studies in healthy individuals suggests that short-lasting emotional states and anxiety affect the later RREP components (N1, P2, P3) related to higher-order neural processing of respiratory sensations, but not the earlier RREP components (Nf, P1) related to first-order sensory processing. We conclude with a discussion of the implications of this work for future research that needs to focus on respiratory patient groups and the associated clinical outcomes.

  9. Basolateral Na+–H+ exchanger-1 in rat taste receptor cells is involved in neural adaptation to acidic stimuli

    Science.gov (United States)

    Lyall, Vijay; Alam, Rammy I; Malik, Shahbaz A; Phan, Tam-Hao T; Vinnikova, Anna K; Heck, Gerard L; DeSimone, John A

    2004-01-01

    The role of basolateral Na+–H+ exchanger isoform-1 (NHE-1) was investigated in neural adaptation of rat taste responses to acidic stimuli, by direct measurement of intracellular pH (pHi) in polarized taste receptor cells (TRCs) and by chorda tympani (CT) taste nerve recordings. In TRCs perfused with CO2/HCO3−-free solution (pH 7.4), removal of basolateral Na+ decreased pHi reversibly and zoniporide, a specific NHE-1 blocker, inhibited the Na+-induced changes in pHi. The spontaneous rate of TRC pHi recovery from NH4Cl pulses was inhibited by basolateral zoniporide with a Ki of 0.33μm. Exposure to basolateral ionomycin, reversibly increased TRC Ca2+, resting pHi, and the spontaneous rate of pHi recovery from an NH4Cl pulse. These effects of Ca2+ on pHi were blocked by zoniporide. In in vivo experiments, topical lingual application of zoniporide increased the magnitude of the CT responses to acetic acid and CO2, but not to HCl. Topical lingual application of ionomycin did not affect the phasic part of the CT responses to acidic stimuli, but decreased the tonic part by 50% of control over a period of about 1 min. This increased adaptation in the CT response was inhibited by zoniporide. Topical lingual application of 8-CPT-cAMP increased the CT responses to HCl, but not to CO2, and acetic acid. In the presence of cAMP, ionomycin increased sensory adaptation to HCl, CO2, and acetic acid. Thus, cAMP and Ca2+ independently modulate CT responses to acidic stimuli. While cAMP enhances TRC apical H+ entry and CT responses to strong acid, an increase in Ca2+ activates NHE-1, and increases neural adaptation to all acidic stimuli. PMID:14724181

  10. Basolateral Na+-H+ exchanger-1 in rat taste receptor cells is involved in neural adaptation to acidic stimuli.

    Science.gov (United States)

    Lyall, Vijay; Alam, Rammy I; Malik, Shahbaz A; Phan, Tam-Hao T; Vinnikova, Anna K; Heck, Gerard L; DeSimone, John A

    2004-04-01

    The role of basolateral Na(+)-H(+) exchanger isoform-1 (NHE-1) was investigated in neural adaptation of rat taste responses to acidic stimuli, by direct measurement of intracellular pH (pH(i)) in polarized taste receptor cells (TRCs) and by chorda tympani (CT) taste nerve recordings. In TRCs perfused with CO(2)/HCO(3)(-)-free solution (pH 7.4), removal of basolateral Na(+) decreased pH(i) reversibly and zoniporide, a specific NHE-1 blocker, inhibited the Na(+)-induced changes in pH(i). The spontaneous rate of TRC pH(i) recovery from NH(4)Cl pulses was inhibited by basolateral zoniporide with a K(i) of 0.33microm. Exposure to basolateral ionomycin, reversibly increased TRC Ca(2+), resting pH(i), and the spontaneous rate of pH(i) recovery from an NH(4)Cl pulse. These effects of Ca(2+) on pH(i) were blocked by zoniporide. In in vivo experiments, topical lingual application of zoniporide increased the magnitude of the CT responses to acetic acid and CO(2), but not to HCl. Topical lingual application of ionomycin did not affect the phasic part of the CT responses to acidic stimuli, but decreased the tonic part by 50% of control over a period of about 1 min. This increased adaptation in the CT response was inhibited by zoniporide. Topical lingual application of 8-CPT-cAMP increased the CT responses to HCl, but not to CO(2), and acetic acid. In the presence of cAMP, ionomycin increased sensory adaptation to HCl, CO(2), and acetic acid. Thus, cAMP and Ca(2+) independently modulate CT responses to acidic stimuli. While cAMP enhances TRC apical H(+) entry and CT responses to strong acid, an increase in Ca(2+) activates NHE-1, and increases neural adaptation to all acidic stimuli.

  11. 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

  12. 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.

  13. Matched Behavioral and Neural Adaptations for Low Sound Level Echolocation in a Gleaning Bat,Antrozous pallidus.

    Science.gov (United States)

    Measor, Kevin R; Leavell, Brian C; Brewton, Dustin H; Rumschlag, Jeffrey; Barber, Jesse R; Razak, Khaleel A

    2017-01-01

    In active sensing, animals make motor adjustments to match sensory inputs to specialized neural circuitry. Here, we describe an active sensing system for sound level processing. The pallid bat uses downward frequency-modulated (FM) sweeps as echolocation calls for general orientation and obstacle avoidance. The bat's auditory cortex contains a region selective for these FM sweeps (FM sweep-selective region, FMSR). We show that the vast majority of FMSR neurons are sensitive and strongly selective for relatively low levels (30-60 dB SPL). Behavioral testing shows that when a flying bat approaches a target, it reduces output call levels to keep echo levels between ∼30 and 55 dB SPL. Thus, the pallid bat behaviorally matches echo levels to an optimized neural representation of sound levels. FMSR neurons are more selective for sound levels of FM sweeps than tones, suggesting that across-frequency integration enhances level tuning. Level-dependent timing of high-frequency sideband inhibition in the receptive field shapes increased level selectivity for FM sweeps. Together with previous studies, these data indicate that the same receptive field properties shape multiple filters (sweep direction, rate, and level) for FM sweeps, a sound common in multiple vocalizations, including human speech. The matched behavioral and neural adaptations for low-intensity echolocation in the pallid bat will facilitate foraging with reduced probability of acoustic detection by prey.

  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...... mechanism uses to learn the target’s velocity via fast correlation-based unsupervised learning. Simulation results for tracking a pure tone acoustic target moving along a semi-circular trajectory validate our approach. Three different angular velocities in three separate trials were employed...

  15. 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.

  16. 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

  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. 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

    2017-02-21

    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.

  19. Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

    Science.gov (United States)

    Chen, Zhenfeng; Ge, Shuzhi Sam; Zhang, Yun; Li, Yanan

    2014-11-01

    This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Luyang Jing

    2017-02-01

    Full Text Available 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.

  3. Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data.

    Science.gov (United States)

    Chen, Yonghong; Bressler, Steven L; Ding, Mingzhou

    2006-01-30

    It is often useful in multivariate time series analysis to determine statistical causal relations between different time series. Granger causality is a fundamental measure for this purpose. Yet the traditional pairwise approach to Granger causality analysis may not clearly distinguish between direct causal influences from one time series to another and indirect ones acting through a third time series. In order to differentiate direct from indirect Granger causality, a conditional Granger causality measure in the frequency domain is derived based on a partition matrix technique. Simulations and an application to neural field potential time series are demonstrated to validate the method.

  4. Adaptive neural network control of fes-induced cyclical lower leg movements

    NARCIS (Netherlands)

    Stroeve, S.H.; Franken, H.M.; Veltink, Petrus H.; van Luenen, W.T.C.

    1992-01-01

    As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging

  5. Social categories shape the neural representation of emotion: evidence from a visual face adaptation task

    NARCIS (Netherlands)

    Otten, M.; Banaji, M.R.

    2012-01-01

    A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the

  6. 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,...

  7. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Luo, Shaohua [School of Automation, Chongqing University, Chongqing 400044 (China); Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021 (China); Wu, Songli [Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021 (China); Gao, Ruizhen [School of Automation, Chongqing University, Chongqing 400044 (China)

    2015-07-15

    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.

  9. 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.

  10. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials.

    Science.gov (United States)

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Millán-Calenti, José Carlos; Del Percio, Claudio; Buján, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between "seen" trials and "not seen" trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expression, and written words). Results showed the typical visual ERP components in both "seen" and "not seen" trials. There was no statistical difference in the ERP peak latencies between the "seen" and "not seen" trials, suggesting a similar timing of the cortical neural synchronization regardless the primary visual consciousness. In contrast, ERP sources showed differences between "seen" and "not seen" trials. For the visuospatial stimuli, the primary consciousness was related to higher activity in dorsal occipital and parietal sources at about 400 ms post-stimulus. For the emotional face expressions, there was greater activity in parietal and frontal sources at about 180 ms post-stimulus. For the written letters, there was higher activity in occipital, parietal and temporal sources at about 230 ms post-stimulus. These results hint that primary visual consciousness is associated with an enhanced cortical neural synchronization having entirely different spatiotemporal characteristics as a function of the features of the visual stimuli and possibly, the relative qualia (i.e., visuospatial, face expression, and words). In this framework, the dorsal visual stream may be synchronized in association with the primary consciousness of visuospatial and emotional face contents. Analogously, both dorsal and ventral visual streams may be synchronized in association with the primary consciousness of linguistic contents. In this line of reasoning, the ensemble of the cortical neural networks

  11. Neural Time Course of Conflict Adaptation Effects on the Stroop Task

    Science.gov (United States)

    Larson, Michael J.; Kaufman, David A. S.; Perlstein, William M.

    2009-01-01

    Cognitive control theory suggests conflict effects are reduced following high- relative to low-conflict trials. Such reactive adjustments in control, frequently termed "conflict adaptation effects," indicate a dynamic interplay between regulative and evaluative components of cognitive control necessary for adaptable goal-directed behavior. The…

  12. 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)

  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. Neural Networks in Nonlinear Aircraft Control

    Science.gov (United States)

    Linse, Dennis J.

    1990-01-01

    Recent research indicates that artificial neural networks offer interesting learning or adaptive capabilities. The current research focuses on the potential for application of neural networks in a nonlinear aircraft control law. The current work has been to determine which networks are suitable for such an application and how they will fit into a nonlinear control law.

  16. Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm

    Directory of Open Access Journals (Sweden)

    Nguyen Ngoc Son

    2016-12-01

    Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.

  17. Investigations on the Potential of Convolutional Neural Networks for Vehicle Classification Based on RGB and LIDAR Data

    Science.gov (United States)

    Niessner, R.; Schilling, H.; Jutzi, B.

    2017-05-01

    In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. Robust Adaptive Tracking Control of the Underwater Robot with Input Nonlinearity Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Mou Chen

    2010-11-01

    Full Text Available In this paper, robust adaptive tracking control is proposed for the underwater robot in the presence of parametric uncertainties and unknown external disturbances. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using parameter adaptation, backstepping control and variable structure based techniques, the robust adaptive tracking control is presented for underwater robots to handle the uncertainties, saturation and dead-zone characteristics of actuators. Actuator nonlinearities comprising of dead-zone and saturation are explicitly considered in the tracking control design. Under the proposed tracking control, semi-global uniform boundedness of the closed-loop signals is guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed robust adaptive tracking control.

  3. 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.

  4. Adaptive potential of a Pacific salmon challenged by climate change

    Science.gov (United States)

    Muñoz, Nicolas J.; Farrell, Anthony P.; Heath, John W.; Neff, Bryan D.

    2015-02-01

    Pacific salmon provide critical sustenance for millions of people worldwide and have far-reaching impacts on the productivity of ecosystems. Rising temperatures now threaten the persistence of these important fishes, yet it remains unknown whether populations can adapt. Here, we provide the first evidence that a Pacific salmon has both physiological and genetic capacities to increase its thermal tolerance in response to rising temperatures. In juvenile chinook salmon (Oncorhynchus tshawytscha), a 4 °C increase in developmental temperature was associated with a 2 °C increase in key measures of the thermal performance of cardiac function. Moreover, additive genetic effects significantly influenced several measures of cardiac capacity, indicative of heritable variation on which selection can act. However, a lack of both plasticity and genetic variation was found for the arrhythmic temperature of the heart, constraining this upper thermal limit to a maximum of 24.5 +/- 2.2 °C. Linking this constraint on thermal tolerance with present-day river temperatures and projected warming scenarios, we predict a 17% chance of catastrophic loss in the population by 2100 based on the average warming projection, with this chance increasing to 98% in the maximum warming scenario. Climate change mitigation is thus necessary to ensure the future viability of Pacific salmon populations.

  5. Data Mining and Neural Networks Based Self-Adaptive Protection Strategies for Distribution Systems with DGs and FCLs

    Directory of Open Access Journals (Sweden)

    Wen-Jun Tang

    2018-02-01

    Full Text Available In light of the development of renewable energy and concerns over environmental protection, distributed generations (DGs have become a trend in distribution systems. In addition, fault current limiters (FCLs may be installed in such systems to prevent the short-circuit current from exceeding the capacity of the power apparatus. However, DGs and FCLs can lead to problems, the most critical of which is miscoordination in protection system. This paper proposes overcurrent protection strategies for distribution systems with DGs and FCLs. Through the proposed approach, relays with communication ability can determine their own operating states with the help of an operation setting decision tree and topology-adaptive neural network model based on data processed through continuous wavelet transform. The performance and effectiveness of the proposed protection strategies are verified by the simulation results obtained from various system topologies with or without DGs, FCLs, and load variations.

  6. 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.

  7. Dynamic brightness induction causes flicker adaptation, but only along the edges: Evidence against the neural filling-in of brightness

    Science.gov (United States)

    Robinson, Alan E.; de Sa, Virginia R.

    2013-01-01

    Is brightness represented in a point-for-point neural map that is filled in from the response of small, contrast-sensitive edge detector cells? We tested for the presence of this filled-in map by adapting to illusory flicker caused by a dynamic brightness-induction stimulus. Thereafter flicker sensitivity was reduced when our test region was the same size as the induced region, but not for smaller, inset regions. This suggests induced brightness is represented by either small edge-selective cells with no filling-in stage, or by contrast-sensitive spatial filters at many different scales, but not by a population of filled-in neurons arranged in a point-for-point map. PMID:23729768

  8. 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.

  9. Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems.

    Science.gov (United States)

    Yan-Jun Liu; Shu Li; Shaocheng Tong; Chen, C L Philip

    2017-07-01

    In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they can be viewed as a class of multi-input multioutput systems in the nonstrict feedback structure. The similar control problem of this class of systems has been addressed in the past, but it focused on the continuous-time systems. Due to the complicacies of the system structure, it will become more difficult for the controller design and the stability analysis. To stabilize this class of systems, a new recursive procedure is developed, and the effect caused by the noncausal problem in the nonstrict feedback DT structure can be solved using a semirecurrent neural approximation. Based on the Lyapunov difference approach, it is proved that all the signals of the closed-loop system are semiglobal, ultimately uniformly bounded, and a good tracking performance can be guaranteed. The feasibility of the proposed controllers can be validated by setting a simulation example.

  10. 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.

  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. 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

  13. Quantifying the adaptive potential of an antibiotic resistance enzyme.

    Directory of Open Access Journals (Sweden)

    Martijn F Schenk

    2012-06-01

    Full Text Available For a quantitative understanding of the process of adaptation, we need to understand its "raw material," that is, the frequency and fitness effects of beneficial mutations. At present, most empirical evidence suggests an exponential distribution of fitness effects of beneficial mutations, as predicted for Gumbel-domain distributions by extreme value theory. Here, we study the distribution of mutation effects on cefotaxime (Ctx resistance and fitness of 48 unique beneficial mutations in the bacterial enzyme TEM-1 β-lactamase, which were obtained by screening the products of random mutagenesis for increased Ctx resistance. Our contributions are threefold. First, based on the frequency of unique mutations among more than 300 sequenced isolates and correcting for mutation bias, we conservatively estimate that the total number of first-step mutations that increase Ctx resistance in this enzyme is 87 [95% CI 75-189], or 3.4% of all 2,583 possible base-pair substitutions. Of the 48 mutations, 10 are synonymous and the majority of the 38 non-synonymous mutations occur in the pocket surrounding the catalytic site. Second, we estimate the effects of the mutations on Ctx resistance by determining survival at various Ctx concentrations, and we derive their fitness effects by modeling reproduction and survival as a branching process. Third, we find that the distribution of both measures follows a Fréchet-type distribution characterized by a broad tail of a few exceptionally fit mutants. Such distributions have fundamental evolutionary implications, including an increased predictability of evolution, and may provide a partial explanation for recent observations of striking parallel evolution of antibiotic resistance.

  14. 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.

  15. Adaptive fuzzy neural network control design via a T-S fuzzy model for a robot manipulator including actuator dynamics.

    Science.gov (United States)

    Wai, Rong-Jong; Yang, Zhi-Wei

    2008-10-01

    This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.

  16. 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.

  17. Multiresolution stroke sketch adaptive representation and neural network processing system for gray-level image recognition

    Science.gov (United States)

    Meystel, Alexander M.; Rybak, Ilya A.; Bhasin, Sanjay

    1992-11-01

    This paper describes a method for multiresolutional representation of gray-level images as hierarchial sets of strokes characterizing forms of objects with different degrees of generalization depending on the context of the image. This method transforms the original image into a hierarchical graph which allows for efficient coding in order to store, retrieve, and recognize the image. The method which is described is based upon finding the resolution levels for each image which minimizes the computations required. This becomes possible because of the use of a special image representation technique called Multiresolutional Attentional Representation for Recognition, based upon a feature which the authors call a stroke. This feature turns out to be efficient in the process of finding the appropriate system of resolutions and construction of the relational graph. Multiresolutional Attentional Representation for Recognition (MARR) is formed by a multi-layer neural network with recurrent inhibitory connections between neurons, the receptive fields of which are selectively tuned to detect the orientation of local contrasts in parts of the image with appropriate degree of generalization. This method simulates the 'coarse-to-fine' algorithm which an artist usually uses, making at attentional sketch of real images. The method, algorithms, and neural network architecture in this system can be used in many machine-vision systems with AI properties; in particular, robotic vision. We expect that systems with MARR can become a component of intelligent control systems for autonomous robots. Their architectures are mostly multiresolutional and match well with the multiple resolutions of the MARR structure.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. 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

    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...... 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...

  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. An adaptive neural mechanism for acoustic motion perception with varying sparsity

    DEFF Research Database (Denmark)

    Shaikh, Danish; Manoonpong, Poramate

    2017-01-01

    .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 k...

  6. 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.

  7. Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification.

    Science.gov (United States)

    Baldwin, Carryl L; Penaranda, B N

    2012-01-02

    Adaptive training using neurophysiological measures requires efficient classification of mental workload in real time as a learner encounters new and increasingly difficult levels of tasks. Previous investigations have shown that artificial neural networks (ANNs) can accurately classify workload, but only when trained on neurophysiological exemplars from experienced operators on specific tasks. The present study examined classification accuracies for ANNs trained on electroencephalographic (EEG) activity recorded while participants performed the same (within task) and different (cross) tasks for short periods of time with little or no prior exposure to the tasks. Participants performed three working memory tasks at two difficulty levels with order of task and difficulty level counterbalanced. Within-task classification accuracies were high when ANNs were trained on exemplars from the same task or a set containing the to-be-classified task, (M=87.1% and 85.3%, respectively). Cross-task classification accuracies were significantly lower (average 44.8%) indicating consistent systematic misclassification for certain tasks in some individuals. Results are discussed in terms of their implications for developing neurophysiologically driven adaptive training platforms. Copyright © 2011 Elsevier Inc. All rights reserved.

  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.

    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.

  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.

    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.

  10. Potential fault region detection in TFDS images based on convolutional neural network

    Science.gov (United States)

    Sun, Junhua; Xiao, Zhongwen

    2016-10-01

    In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

  11. Fluid Intelligence and Automatic Neural Processes in Facial Expression Perception: An Event-Related Potential Study.

    Science.gov (United States)

    Liu, Tongran; Xiao, Tong; Li, Xiaoyan; Shi, Jiannong

    2015-01-01

    The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent males were enrolled: a high IQ group and an average IQ group. Age and parental socioeconomic status were matched between the two groups. Participants counted the numbers of the central cross changes while paired facial expressions were presented bilaterally in an oddball paradigm. There were two experimental conditions: a happy condition, in which neutral expressions were standard stimuli (p = 0.8) and happy expressions were deviant stimuli (p = 0.2), and a fearful condition, in which neutral expressions were standard stimuli (p = 0.8) and fearful expressions were deviant stimuli (p = 0.2). Participants were required to concentrate on the primary task of counting the central cross changes and to ignore the expressions to ensure that facial expression processing was automatic. Event-related potentials (ERPs) were obtained during the tasks. The visual mismatch negativity (vMMN) components were analyzed to index the automatic neural processing of facial expressions. For the early vMMN (50-130 ms), the high IQ group showed more negative vMMN amplitudes than the average IQ group in the happy condition. For the late vMMN (320-450 ms), the high IQ group had greater vMMN responses than the average IQ group over frontal and occipito-temporal areas in the fearful condition, and the average IQ group evoked larger vMMN amplitudes than the high IQ group over occipito-temporal areas in the happy condition. The present study elucidated the close relationships between fluid intelligence and pre-attentive change detection on social-emotional information.

  12. 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

  13. Fluid Intelligence and Automatic Neural Processes in Facial Expression Perception: An Event-Related Potential Study.

    Directory of Open Access Journals (Sweden)

    Tongran Liu

    Full Text Available The relationship between human fluid intelligence and social-emotional abilities has been a topic of considerable interest. The current study investigated whether adolescents with different intellectual levels had different automatic neural processing of facial expressions. Two groups of adolescent males were enrolled: a high IQ group and an average IQ group. Age and parental socioeconomic status were matched between the two groups. Participants counted the numbers of the central cross changes while paired facial expressions were presented bilaterally in an oddball paradigm. There were two experimental conditions: a happy condition, in which neutral expressions were standard stimuli (p = 0.8 and happy expressions were deviant stimuli (p = 0.2, and a fearful condition, in which neutral expressions were standard stimuli (p = 0.8 and fearful expressions were deviant stimuli (p = 0.2. Participants were required to concentrate on the primary task of counting the central cross changes and to ignore the expressions to ensure that facial expression processing was automatic. Event-related potentials (ERPs were obtained during the tasks. The visual mismatch negativity (vMMN components were analyzed to index the automatic neural processing of facial expressions. For the early vMMN (50-130 ms, the high IQ group showed more negative vMMN amplitudes than the average IQ group in the happy condition. For the late vMMN (320-450 ms, the high IQ group had greater vMMN responses than the average IQ group over frontal and occipito-temporal areas in the fearful condition, and the average IQ group evoked larger vMMN amplitudes than the high IQ group over occipito-temporal areas in the happy condition. The present study elucidated the close relationships between fluid intelligence and pre-attentive change detection on social-emotional information.

  14. Introduction beyond a species range: a relationship between population origin, adaptive potential and plant performance.

    Science.gov (United States)

    Volis, S; Ormanbekova, D; Yermekbayev, K; Song, M; Shulgina, I

    2014-09-01

    The adaptive potential of a population defines its importance for species survival in changing environmental conditions such as global climate change. Very few empirical studies have examined adaptive potential across species' ranges, namely, of edge vs core populations, and we are unaware of a study that has tested adaptive potential (namely, variation in adaptive traits) and measured performance of such populations in conditions not currently experienced by the species but expected in the future. Here we report the results of a Triticum dicoccoides population study that employed transplant experiments and analysis of quantitative trait variation. Two populations at the opposite edges of the species range (1) were locally adapted; (2) had lower adaptive potential (inferred from the extent of genetic quantitative trait variation) than the two core populations; and (3) were outperformed by the plants from the core population in the novel environment. The fact that plants from the species arid edge performed worse than plants from the more mesic core in extreme drought conditions beyond the present climatic envelope of the species implies that usage of peripheral populations for conservation purposes must be based on intensive sampling of among-population variation.

  15. Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks.

    Science.gov (United States)

    Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali

    2015-10-01

    This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear systems, in this paper, tracking control problem is solved for uncertain nonaffine nonlinear systems with input saturation. To deal with the saturation constraint, an auxiliary system is constructed and a modified tracking error is defined. Then, by employing implicit function theorem, mean value theorem, and modified tracking error, updating rules are derived based on the well-known back-propagation (BP) algorithm, which has been proven to be the most relevant updating rule to control problems. However, most of the previous approaches on BP algorithm suffer from lack of stability analysis. By injecting a damping term to the standard BP algorithm, uniformly ultimately boundedness of all the signals of the closed-loop system is ensured via Lyapunov's direct method. Furthermore, the presented approach employs nonlinear in parameter neural networks. Hence, the proposed scheme is applicable to systems with higher degrees of nonlinearity. Using a high-gain observer to reconstruct the states of the system, an output feedback controller is also presented. Finally, the simulation results performed on a Duffing-Holmes chaotic system, a generalized pendulum-type system, and a numerical system are presented to demonstrate the effectiveness of the suggested state and output feedback control schemes.

  16. 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.

  17. Neural-network-based adaptive UPFC for improving transient stability performance of power system.

    Science.gov (United States)

    Mishra, Sukumar

    2006-03-01

    This paper uses the recently proposed H(infinity)-learning method, for updating the parameter of the radial basis function neural network (RBFNN) used as a control scheme for the unified power flow controller (UPFC) to improve the transient stability performance of a multimachine power system. The RBFNN uses a single neuron architecture whose input is proportional to the difference in error and the updating of its parameters is carried via a proportional value of the error. Also, the coefficients of the difference of error, error, and auxiliary signal used for improving damping performance are depicted by a genetic algorithm. The performance of the newly designed controller is evaluated in a four-machine power system subjected to different types of disturbances. The newly designed single-neuron RBFNN-based UPFC exhibits better damping performance compared to the conventional PID as well as the extended Kalman filter (EKF) updating-based RBFNN scheme, making the unstable cases stable. Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Also, all the machines are being equipped with the conventional power system stabilizer (PSS) to study the coordinated effect of UPFC and PSS in the system.

  18. 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.

  19. 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)

  20. 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.

  1. 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.

    2017-05-01

    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 (Au147), 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 Au147, 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 Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

  2. 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.                       

  3. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147nanocluster.

    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.

  4. Behavioral and neural responses of toads to salt solutions correlate with basolateral membrane potential of epidermal cells of the skin

    DEFF Research Database (Denmark)

    Hillyard, Stanley D; Baula, Victor; Tuttle, Wendy

    2007-01-01

    low, V(b) transiently hyperpolarized to values near the equilibrium potential for K(+) and corresponded with the reduced neural response. These results support the hypothesis that chemosensory function of the skin is analogous to that of mammalian taste cells but utilizes paracellular ion transport...

  5. 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

  6. Neural Correlates of Abstract Rule Learning: An Event-Related Potential Study

    Science.gov (United States)

    Sun, Fang; Hoshi-Shiba, Reiko; Abla, Dilshat; Okanoya, Kazuo

    2012-01-01

    Abstract rule learning is a fundamental aspect of human cognition, and is essential for language acquisition. However, despite its importance, the neural mechanisms underlying abstract rule learning are still largely unclear. In this study, we investigated the neural correlates of abstract rule learning by recording auditory event-related…

  7. 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

  8. Mind your errors: evidence for a neural mechanism linking growth mind-set to adaptive posterror adjustments.

    Science.gov (United States)

    Moser, Jason S; Schroder, Hans S; Heeter, Carrie; Moran, Tim P; Lee, Yu-Hao

    2011-12-01

    How well people bounce back from mistakes depends on their beliefs about learning and intelligence. For individuals with a growth mind-set, who believe intelligence develops through effort, mistakes are seen as opportunities to learn and improve. For individuals with a fixed mind-set, who believe intelligence is a stable characteristic, mistakes indicate lack of ability. We examined performance-monitoring event-related potentials (ERPs) to probe the neural mechanisms underlying these different reactions to mistakes. Findings revealed that a growth mind-set was associated with enhancement of the error positivity component (Pe), which reflects awareness of and allocation of attention to mistakes. More growth-minded individuals also showed superior accuracy after mistakes compared with individuals endorsing a more fixed mind-set. It is critical to note that Pe amplitude mediated the relationship between mind-set and posterror accuracy. These results suggest that neural mechanisms indexing on-line awareness of and attention to mistakes are intimately involved in growth-minded individuals' ability to rebound from mistakes.

  9. 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.

  10. The steady-state visual evoked potential reveals neural correlates of the items encoded into visual working memory.

    Science.gov (United States)

    Peterson, Dwight J; Gurariy, Gennadiy; Dimotsantos, Gabriella G; Arciniega, Hector; Berryhill, Marian E; Caplovitz, Gideon P

    2014-10-01

    Visual working memory (VWM) capacity limitations are estimated to be ~4 items. Yet, it remains unclear why certain items from a given memory array may be successfully retrieved from VWM and others are lost. Existing measures of the neural correlates of VWM cannot address this question because they measure the aggregate processing of the entire stimulus array rather than neural signatures of individual items. Moreover, this cumulative processing is usually measured during the delay period, thereby reflecting the allocation of neural resources during VWM maintenance. Here, we use the steady-state visual evoked potential (SSVEP) to identify the neural correlates of individual stimuli at VWM encoding and test two distinct hypotheses: the focused-resource hypothesis and the diffuse-resource hypothesis, for how the allocation of neural resources during VWM encoding may contribute to VWM capacity limitations. First, we found that SSVEP amplitudes were larger for stimuli that were later remembered than for items that were subsequently forgotten. Second, this pattern generalized so that the SSVEP amplitudes were also larger for the unprobed stimuli in correct compared to incorrect trials. These data are consistent with the diffuse-resource view in which attentional resources are broadly allocated across the whole stimulus array. These results illustrate the important role encoding mechanisms play in limiting the capacity of VWM. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. 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.

  12. 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

  13. 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)

  14. PSYCHO-EMOTIONAL CHARACTERISTIC OF YOUNG GENERATION WITH A VARYING ADAPTATION POTENTIAL

    Directory of Open Access Journals (Sweden)

    Анатолий Степанович Пуликов

    2013-08-01

    Full Text Available Goal:  to study certain characteristics of emotional constitution of young generation in Siberia and their dependence on adaptive capability.Method and methodology:  124 apparently healthy young male students of Zheleznogorsk branch ofKrasnoyarskStatePedagogicalUniversity named after V.P. Astafyev were examined on a volunteer basis upon their informed consent.Up-to-date conventional techniques were used for making anthropometric measurements and conducting functional study.Personality questionnaire by G.Y. Eysenck was used for referring students to emotional types.Results: body weight of young males from Zheleznogorsk increases from asthenic to normosthenic and picnic types of build, while the height decreases from normosthenic and asthenic to picnic types. About 40% of young males are of normosthenic build and about 30% each can be referred to picnic or asthenic types.As far as adaptation potential is concerned, in the majority of young males strain of adaptation mechanisms or poor adaptation (90-92% are present, and only 8-10% have normal adaptation characteristics.Parameters of adaptation capabilities in characteristics of emotional status adequately reflect functional activity of central links of regulation and adaptation ‘resources’. Among characteristic features of emotional constitution of young males from Zheleznogorsk are moderate and considerable introversion, a high degree of emotional stability in situations when adaptation mechanisms are strained, and rather high emotional instability among young males with normal adaptation characteristics and even higher among those with poor adaptation mechanisms. All these are probably connected with radioecological situation at Zheleznogorsk.Scope of application of results: medicine, psychology, age-specific physiology, anthropology, neurology.DOI: http://dx.doi.org/10.12731/2218-7405-2013-6-21

  15. 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)

  16. 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.

  17. 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.

  18. 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

  19. When the Sense of Smell Meets Emotion: Anxiety-State-Dependent Olfactory Processing and Neural Circuitry Adaptation

    Science.gov (United States)

    Novak, Lucas R.; Gitelman, Darren R.

    2013-01-01

    Phylogenetically the most ancient sense, olfaction is characterized by a unique intimacy with the emotion system. However, mechanisms underlying olfaction–emotion interaction remain unclear, especially in an ever-changing environment and dynamic internal milieu. Perturbing the internal state with anxiety induction in human subjects, we interrogated emotion-state-dependent olfactory processing in a functional magnetic resonance imaging (fMRI) study. Following anxiety induction, initially neutral odors become unpleasant and take longer to detect, accompanied by augmented response to these odors in the olfactory (anterior piriform and orbitofrontal) cortices and emotion-relevant pregenual anterior cingulate cortex. In parallel, the olfactory sensory relay adapts with increased anxiety, incorporating amygdala as an integral step via strengthened (afferent or efferent) connections between amygdala and all levels of the olfactory cortical hierarchy. This anxiety-state-dependent neural circuitry thus enables cumulative infusion of limbic affective information throughout the olfactory sensory progression, thereby driving affectively charged olfactory perception. These findings could constitute an olfactory etiology model of emotional disorders, as exaggerated emotion–olfaction interaction in negative mood states turns innocuous odors aversive, fueling anxiety and depression with rising ambient sensory stress. PMID:24068799

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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)

  8. 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.

  9. 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.

  10. 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.

  11. Cardiovascular state changes during performance of a simulated ambulance dispatchers' task : Potential use for adaptive support

    NARCIS (Netherlands)

    Mulder, L. J. M.; Dijksterhuis, C.; Stuiver, A.; de Waard, D.

    2009-01-01

    Adaptive support has the potential to keep the operator optimally motivated, involved, and able to perform a task. in order to use such support, the operator's state has to be determined from physiological parameters and task performance measures. In an environment where the task of an ambulance

  12. 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

  13. 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.

  14. 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.

  15. Adaptive decoding using local field potentials in a brain-machine interface.

    Science.gov (United States)

    Rosa So; Libedinsky, Camilo; Kai Keng Ang; Wee Chiek Clement Lim; Kyaw Kyar Toe; Cuntai Guan

    2016-08-01

    Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model. We implemented a BMI system in a nonhuman primate model that allows brain-controlled movement of a robotic platform. Using this system, we showed that LFP signals alone can be used for decoding in a closed-loop brain-controlled BMI. Further, we performed offline analysis to assess the potential implementation of an adaptive decoding method that does not presume knowledge of the target location. Our results show that with periodic signal and channel selection adaptation, decoding accuracy using LFP alone can be improved by between 5-50%. These results demonstrate the feasibility of implementing unsupervised adaptive methods during asynchronous decoding of LFP signals for long-term usage in a BMI system.

  16. 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.

  17. 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.)

  18. Potential neural mechanisms underlying the effectiveness of early intervention for children with autism spectrum disorder

    Science.gov (United States)

    Sullivan, Katherine; Stone, Wendy L.; Dawson, Geraldine

    2014-01-01

    Although evidence supports the efficacy of early intervention for improving outcomes for children with autism spectrum disorder (ASD), the mechanisms underlying their effectiveness remain poorly understood. This paper reviews the research literature on the neural bases of the early core deficits in ASD and proposes three key features of early intervention related to the neural mechanisms that may contribute to its effectiveness in improving deficit areas. These features include (1) the early onset of intensive intervention which capitalizes on the experience-expectant plasticity of the immature brain, (2) the use of treatment strategies that address core deficits in social motivation through an emphasis on positive social engagement and arousal modulation, and (3) promotion of complex neural networks and connectivity through thematic, multi-sensory and multi-domain teaching approaches. Understanding the mechanisms of effective early intervention will enable us to identify common or foundational active ingredients for promoting optimal outcomes in children with ASD. PMID:25108609

  19. 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.

  20. Adaptation

    International Development Research Centre (IDRC) Digital Library (Canada)

    . Dar es Salaam. Durban. Bloemfontein. Antananarivo. Cape Town. Ifrane ... program strategy. A number of CCAA-supported projects have relevance to other important adaptation-related themes such as disaster preparedness and climate.

  1. History, rationale, and lessons learned: Thresholds of potential concern in Kruger National Park river adaptive management

    Directory of Open Access Journals (Sweden)

    Craig A. McLoughlin

    2011-05-01

    Full Text Available The Kruger National Park’s (KNP adopted system of management, called Strategic Adaptive Management (SAM, originated during the Kruger National Park Rivers Research Programme (KNPRRP of the 1990s. An important concept in SAM is the thresholds of potential concern (TPCs, representing end-points in a continuum of change. TPCs within the KNP SAM system guide management if or when reached, ‘red-flagging’ possible negative biodiversity impacts and catalysing consideration of management options. TPC-related monitoring generates the strategic information for ongoing evaluation, learning and adaptation within SAM. Post- KNPRRP, although river flow and water quality TPCs have been implemented partly, those designed to detect undesirable changes in biodiversity have not been implemented, until recently. This paper describes the history, rationale, application and ongoing developments associated with the KNP river TPCs over the last decade, providing some key lessons for organisations utilising SAM. The paper concludes with an overview of new thinking and future directions envisaged for the KNP river TPCs, as part of the KNP SAM system. Conservation implications: This paper documents important concepts of strategic adaptive management associated with the KNP river systems. Understanding, related to the rationale and justification for use and development or refinement of the thresholds of potential concern, lays an important foundation for ongoing work in managing these rivers adaptively.

  2. Neural Network Detection of Ventricular Late Potentials in ECG Signals Using Wavelet Transform Extracted Parameters

    National Research Council Canada - National Science Library

    Mousa, Ayad

    2001-01-01

    .... In this work, discrete Wavelet Transform (DWT) and Artificial Neural Networks (ANN) are applied in the analysis of ECG signals in order to identify VLPs, Results of this analysis are used to classify patients with and without VLPs in their ECGs...

  3. Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input.

    Science.gov (United States)

    Liu, Yan-Jun; Zhou, Ning

    2010-10-01

    Based on the universal approximation property of the fuzzy-neural networks, an adaptive fuzzy-neural observer design algorithm is studied for a class of nonlinear SISO systems with both a completely unknown function and an unknown dead-zone input. The fuzzy-neural networks are used to approximate the unknown nonlinear function. Because it is assumed that the system states are unmeasured, an observer needs to be designed to estimate those unmeasured states. In the previous works with the observer design based on the universal approximator, when the dead-zone input appears it is ignored and the stability of the closed-loop system will be affected. In this paper, the proposed algorithm overcomes the affections of dead-zone input for the stability of the systems. Moreover, the dead-zone parameters are assumed to be unknown and will be adjusted adaptively as well as the sign function being introduced to compensate the dead-zone. With the aid of the Lyapunov analysis method, the stability of the closed-loop system is proven. A simulation example is provided to illustrate the feasibility of the control algorithm presented in this paper. Copyright © 2010. Published by Elsevier Ltd.

  4. Myelin plasticity, neural activity, and traumatic neural injury.

    Science.gov (United States)

    Kondiles, Bethany R; Horner, Philip J

    2018-02-01

    The possibility that adult organisms exhibit myelin plasticity has recently become a topic of great interest. Many researchers are exploring the role of myelin growth and adaptation in daily functions such as memory and motor learning. Here we consider evidence for three different potential categories of myelin plasticity: the myelination of previously bare axons, remodeling of existing sheaths, and the removal of a sheath with replacement by a new internode. We also review evidence that points to the importance of neural activity as a mechanism by which oligodendrocyte precursor cells (OPCs) are cued to differentiate into myelinating oligodendrocytes, which may potentially be an important component of myelin plasticity. Finally, we discuss demyelination in the context of traumatic neural injury and present an argument for altering neural activity as a potential therapeutic target for remyelination following injury. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 108-122, 2018. © 2017 Wiley Periodicals, Inc.

  5. Shaped by the environment--adaptation in plants: meeting report based on the presentations at the FEBS Workshop 'Adaptation Potential in Plants' 2009 (Vienna, Austria).

    Science.gov (United States)

    Siomos, Maria F

    2009-09-01

    As sessile organisms that are unable to escape from inhospitable environments, plants are at the mercy of the elements. Nonetheless, plants have managed to adapt, evolve and survive in some of the harshest conditions on earth. The FEBS Workshop 'Adaptation Potential in Plants', held at the Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria from 19 to 21 March 2009, provided a forum (including 18 invited talks, 8 selected short talks and 69 posters) for about 100 plant biologists from 32 countries, working in the diverse fields of genetics, epigenetics, stress signalling, and growth and development, to come together and discuss adaptation potential in plants at all its levels.

  6. 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

  7. Savings Potential of ENERGY STAR(R) External Power Adapters andBattery Chargers

    Energy Technology Data Exchange (ETDEWEB)

    Webber, Carrie; Korn, David; Sanchez, Marla

    2007-02-28

    External power adapters may lose 10 to 70 percent of theenergy they consume, dissipated as heat rather than converted into usefulenergy. Battery charging systems have more avenues for losses: inaddition to power conversion losses, power is consumed by the chargingcircuitry, and additional power may be needed after the battery is fullcharged to balance self-discharge. In 2005, the Environmental ProtectionAgency launched a new ENERGY STAR(R) label for external power supplies(EPSs) that convert line-voltage AC electricity into low-voltage DCelectricity for certain electronic devices. The specification includedpower supplies for products with battery charging functions (e.g. laptopsand cell phones), but excluded others. In January 2006, a separatespecification was issued for battery charging systems contained primarilyin small household appliances and power tools. In addition to the ENERGYSTAR(R) label, the state of California will implement minimum energyperformance standards for EPSs in 2007, and similar standards for EPSsand battery chargers are in development at the national level.Many of theproducts covered by these policies use relatively little power and havemodest per-unit savings potential compared to conventional energyefficiency targets. But with an estimated 1.5 billion adapters and 230million battery charging systems in use in the United States, theaggregate savings potential is quite high. This paper presents estimatesof the savings potential for external power adapters and battery chargingsystems through 2025.

  8. 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

  9. Environmental disruption of Host-Microbe co-adaptation as a potential driving force in evolution

    Directory of Open Access Journals (Sweden)

    Yoav eSoen

    2014-06-01

    Full Text Available The microbiome is known to have a profound effect on the development, physiology and health of its host. Whether and how it also contributes to evolutionary diversification of the host is, however, unclear. Here we hypothesize that disruption of the microbiome by new stressful environments interferes with host-microbe co-adaption, contributes to host destabilization, and can drive irreversible changes in the host prior to its genetic adaptation. This hypothesis is based on 3 presumptions: (1 The microbiome consists of heritable partners which contribute to the stability (canalization of host development and physiology in frequently encountered environments, (2 Upon encountering a stressful new environment, the microbiome adapts much faster than the host, and (3 This differential response disrupts cooperation, contributes to host destabilization and promotes reciprocal changes in the host and its microbiome. This dynamic imbalance relaxes as the host and its microbiome establish a new equilibrium state in which they are adapted to one another and to the altered environment. Over long time in this new environment, the changes in the microbiome contribute to the canalization of the altered state. This scenario supports stability of the adapted patterns, while promoting variability which may be beneficial in new stressful conditions, thus allowing the organism to balance stability and flexibility based on contextual demand. Additionally, interaction between heritable microbial (and/or epigenetic changes can promote new outcomes which persist over a wide range of timescales. A sufficiently persistent stress can further induce irreversible changes in the microbiome which may permanently alter the organism prior to genetic changes in the host. Epigenetic and microbial changes therefore provide a potential infrastructure for causal links between immediate responses to new environments and longer-term establishment of evolutionary adaptations.

  10. 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.

  11. 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.

  12. Integrated control of wind farms, FACTS devices and the power network using neural networks and adaptive critic designs

    Science.gov (United States)

    Qiao, Wei

    Worldwide concern about the environmental problems and a possible energy crisis has led to increasing interest in clean and renewable energy generation. Among various renewable energy sources, wind power is the most rapidly growing one. Therefore, how to provide efficient, reliable, and high-performance wind power generation and distribution has become an important and practical issue in the power industry. In addition, because of the new constraints placed by the environmental and economical factors, the trend of power system planning and operation is toward maximum utilization of the existing infrastructure with tight system operating and stability margins. This trend, together with the increased penetration of renewable energy sources, will bring new challenges to power system operation, control, stability and reliability which require innovative solutions. Flexible ac transmission system (FACTS) devices, through their fast, flexible, and effective control capability, provide one possible solution to these challenges. To fully utilize the capability of individual power system components, e.g., wind turbine generators (WTGs) and FACTS devices, their control systems must be suitably designed with high reliability. Moreover, in order to optimize local as well as system-wide performance and stability of the power system, real-time local and wide-area coordinated control is becoming an important issue. Power systems containing conventional synchronous generators, WTGs, and FACTS devices are large-scale, nonlinear, nonstationary, stochastic and complex systems distributed over large geographic areas. Traditional mathematical tools and system control techniques have limitations to control such complex systems to achieve an optimal performance. Intelligent and bio-inspired techniques, such as swarm intelligence, neural networks, and adaptive critic designs, are emerging as promising alternative technologies for power system control and performance optimization. This

  13. Do infants represent the face in a viewpoint-invariant manner? Neural adaptation study as measured by near-infrared spectroscopy

    Directory of Open Access Journals (Sweden)

    Megumi eKobayashi

    2011-11-01

    Full Text Available Recent adult fMRI studies reported that face sensitive cortical areas showed attenuated responses to the repeated presentation of an identical facial image compared to the presentation of different facial images (fMRI-adaptation effects: e.g., Andrews & Ewbank, 2004. Building upon this finding, the current study, employing the adaptation paradigm, used NIRS to explore the neural basis of face processing in infants. In Experiment 1, we compared hemodynamic responses in the bilateral temporal regions during the repeated presentation of the same face (the same-face condition and the sequential presentation of different faces (the different-face condition. We found that 1 hemodynamic responses in the channels around the T5 and T6 regions increased during the presentation of different faces compared to those during the presentation of different objects; and that 2 these channels showed significantly lower response in the same-face condition than in the different-face condition, demonstrating the neural adaptation effect in 5- to 8-month-olds as measured by NIRS. In Experiment 2, when faces in both the same-face and different-face conditions were changed in viewpoint, lower hemodynamic responses in the same-face condition were found in 7- to 8-month-olds but not in 5- to 6-month-olds. Our results suggest that faces are represented in a viewpoint-invariant manner in 7- and 8-month-old infants.

  14. A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

    OpenAIRE

    Ma, Shuang; Liu, Jing; Chen, Chang Wen

    2017-01-01

    Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the origina...

  15. An investigation of potential neural correlates of intrusive retrieval of distressing memories.

    Science.gov (United States)

    Battaglini, Eva; Liddell, Belinda J; Das, Pritha; Malhi, Gin S; Felmingham, Kim; Bryant, Richard A

    2018-03-01

    Despite the prevalence of intrusive memories across psychological disorders, little is known about the neural networks that underpin this form of memory. This study used functional magnetic resonance imaging (fMRI) to identify neural circuits associated with the retrieval of intrusive memories. Participants with moderate levels of anxiety (N = 30) underwent a cold pressor task to induce a physiological stress response, after which they viewed 10 neutral and 10 negative film clips. In a method designed to induce intrusive memories, participants then completed an fMRI scan in which they viewed short (2 s) depictions of neutral components from the original film clips. There were no significant differences in activations during intrusion and non-intrusion responses. Exploratory analyses comparing intrusive responses to neutral stimuli found the insula, inferior frontal gyrus, precuneus, right cerebellum and bilateral supplementary motor area were uniquely activated during experience of intrusions (compared to the neutral cue baseline), whereas no significant activations were in response to negative scenes that did not trigger intrusions. This study did not compare the different neural processes implicated in intrusive and intentional emotional memories. The limited intrusions that could be elicited in the scanning environment restricted the number of trials that could be employed. Although no differences in neural activations were observed between intrusive and non-intrusive responses, the observation of precuneus involvement is consistent with models that propose that intrusive memories are impacted by the extent to which there is contextual integration of the relevant memories. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. 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.

  17. Adaptation potential of naturally ventilated barns to high temperature extremes: The OptiBarn project

    Science.gov (United States)

    Menz, Christoph

    2016-04-01

    Climate change interferes with various aspects of the socio-economic system. One important aspect is its influence on animal husbandry, especially dairy faming. Dairy cows are usually kept in naturally ventilated barns (NVBs) which are particular vulnerable to extreme events due to their low adaptation capabilities. An effective adaptation to high outdoor temperatures for example, is only possible under certain wind and humidity conditions. High temperature extremes are expected to increase in number and strength under climate change. To assess the impact of this change on NVBs and dairy cows also the changes in wind and humidity needs to be considered. Hence we need to consider the multivariate structure of future temperature extremes. The OptiBarn project aims to develop sustainable adaptation strategies for dairy housings under climate change for Europe, by considering the multivariate structure of high temperature extremes. In a first step we identify various multivariate high temperature extremes for three core regions in Europe. With respect to dairy cows in NVBs we will focus on the wind and humidity field during high temperature events. In a second step we will use the CORDEX-EUR-11 ensemble to evaluate the capability of the RCMs to model such events and assess their future change potential. By transferring the outdoor conditions to indoor climate and animal wellbeing the results of this assessment can be used to develop technical, architectural and animal specific adaptation strategies for high temperature extremes.

  18. Neural Induction Potential and MRI of ADSCs Labeled Cationic Superparamagnetic Iron Oxide Nanoparticle In Vitro

    Directory of Open Access Journals (Sweden)

    Weiqiong Ma

    2018-01-01

    Full Text Available Magnetic resonance imaging (MRI combined with contrast agents is believed to be useful for stem cell tracking in vivo, and the aim of this research was to investigate the biosafety and neural induction of SD rat-originated adipose derived stem cells (ADSCs using cationic superparamagnetic iron oxide (SPIO nanoparticle which was synthesized by the improved polyol method, in order to allow visualization using in vitro MRI. The scan protocols were performed with T2-mapping sequence; meanwhile, the ultrastructure of labeled cells was observed by transmission electron microscopy (TEM while the iron content was measured by inductively coupled plasma-atomic emission spectrometry (ICP-AES. After neural induction, nestin and NSE (neural markers were obviously expressed. In vitro MRI showed that the cationic PEG/PEI-modified SPIO nanoparticles could achieve great relaxation performance and favourable longevity. And the ICP-AES quantified the lowest iron content that could be detected by MRI as 1.56~1.8 pg/cell. This study showed that the cationic SPIO could be directly used to label ADSCs, which could then inductively differentiate into nerve and be imaged by in vitro MRI, which would exhibit important guiding significance for the further in vivo MRI towards animal models with neurodegenerative disorders.

  19. 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

  20. Abnormal neural connectivity in schizophrenia and fMRI-brain computer interface as a potential therapeutic approach

    Directory of Open Access Journals (Sweden)

    Sergio eRuiz

    2013-03-01

    Full Text Available Considering that single locations of structural and functional abnormalities are insufficient to explain the diverse psychopathology of schizophrenia, new models have postulated that the impairments associated with the disease arise from a failure to integrate the activity of local and distributed neural circuits: the abnormal neural connectivity hypothesis. In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia.The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.

  1. Adaptation

    International Development Research Centre (IDRC) Digital Library (Canada)

    Nairobi, Kenya. 28 Adapting Fishing Policy to Climate Change with the Aid of Scientific and Endogenous Knowledge. Cap Verde, Gambia,. Guinea, Guinea Bissau,. Mauritania and Senegal. Environment and Development in the Third World. (ENDA-TM). Dakar, Senegal. 29 Integrating Indigenous Knowledge in Climate Risk ...

  2. Do early neural correlates of visual consciousness show the oblique effect? A binocular rivalry and event-related potential study.

    Science.gov (United States)

    Jack, Bradley N; Roeber, Urte; O'Shea, Robert P

    2017-01-01

    When dissimilar images are presented one to each eye, we do not see both images; rather, we see one at a time, alternating unpredictably. This is called binocular rivalry, and it has recently been used to study brain processes that correlate with visual consciousness, because perception changes without any change in the sensory input. Such studies have used various types of images, but the most popular have been gratings: sets of bright and dark lines of orthogonal orientations presented one to each eye. We studied whether using cardinal rival gratings (vertical, 0°, and horizontal, 90°) versus oblique rival gratings (left-oblique, -45°, and right-oblique, 45°) influences early neural correlates of visual consciousness, because of the oblique effect: the tendency for visual performance to be greater for cardinal gratings than for oblique gratings. Participants viewed rival gratings and pressed keys indicating which of the two gratings they perceived, was dominant. Next, we changed one of the gratings to match the grating shown to the other eye, yielding binocular fusion. Participants perceived the rivalry-to-fusion change to the dominant grating and not to the other, suppressed grating. Using event-related potentials (ERPs), we found neural correlates of visual consciousness at the P1 for both sets of gratings, as well as at the P1-N1 for oblique gratings, and we found a neural correlate of the oblique effect at the N1, but only for perceived changes. These results show that the P1 is the earliest neural activity associated with visual consciousness and that visual consciousness might be necessary to elicit the oblique effect.

  3. Do early neural correlates of visual consciousness show the oblique effect? A binocular rivalry and event-related potential study

    Science.gov (United States)

    Roeber, Urte; O’Shea, Robert P.

    2017-01-01

    When dissimilar images are presented one to each eye, we do not see both images; rather, we see one at a time, alternating unpredictably. This is called binocular rivalry, and it has recently been used to study brain processes that correlate with visual consciousness, because perception changes without any change in the sensory input. Such studies have used various types of images, but the most popular have been gratings: sets of bright and dark lines of orthogonal orientations presented one to each eye. We studied whether using cardinal rival gratings (vertical, 0°, and horizontal, 90°) versus oblique rival gratings (left-oblique, –45°, and right-oblique, 45°) influences early neural correlates of visual consciousness, because of the oblique effect: the tendency for visual performance to be greater for cardinal gratings than for oblique gratings. Participants viewed rival gratings and pressed keys indicating which of the two gratings they perceived, was dominant. Next, we changed one of the gratings to match the grating shown to the other eye, yielding binocular fusion. Participants perceived the rivalry-to-fusion change to the dominant grating and not to the other, suppressed grating. Using event-related potentials (ERPs), we found neural correlates of visual consciousness at the P1 for both sets of gratings, as well as at the P1-N1 for oblique gratings, and we found a neural correlate of the oblique effect at the N1, but only for perceived changes. These results show that the P1 is the earliest neural activity associated with visual consciousness and that visual consciousness might be necessary to elicit the oblique effect. PMID:29232704

  4. 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.

  5. Adaptive microbial population shifts in response to a continuous ethanol blend release increases biodegradation potential

    International Nuclear Information System (INIS)

    Ma, Jie; Nossa, Carlos W.; Xiu, Zongming; Rixey, William G.; Alvarez, Pedro J.J.

    2013-01-01

    The fate of fuel releases largely depends on the poorly-understood response in microbial community structure and function. Here, we evaluate the impacts to the microbial community resulting from a pilot-scale continuous release (10 months) of a 10% v:v ethanol solution mixed with benzene and toluene (50 mg/L each). Microbial population shifts were characterized by pyrosequencing-based 16S rRNA analysis and by quantitative PCR targeting Bacteria, Archaea, and functional genes for methanogenesis (mcrA), acetogenesis (fhs) and aerobic degradation of aromatic hydrocarbons (PHE), which could occur in hypoxic micro-environments. The release stimulated microbial growth, increased species richness and diversity, and selected for genotypes involved in fermentative degradation (the relative abundance of mcrA and fhs increased 18- and 6-fold, respectively). The growth of putative hydrocarbon degraders and commensal anaerobes, and increases in microbial diversity and in degradation rates suggest an adaptive response that increases the potential for natural attenuation of ethanol blend releases. -- Highlights: •Pyrosequencing discerned microbial community changes after an ethanol blend release. •Adaptive microbial population shifts that enhance bioremediation were observed. •Hydrocarbon degraders and fermentation syntrophs proliferated. •Surprisingly, both species richness and taxonomic diversity increased. -- Pyrosequencing analysis discerned adaptive microbial population shifts that increase natural attenuation potential of an ethanol-blended fuel release

  6. 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.

  7. 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.

  8. Ischemic long-term-potentiation (iLTP): perspectives to set the threshold of neural plasticity toward therapy.

    Science.gov (United States)

    Lenz, Maximilian; Vlachos, Andreas; Maggio, Nicola

    2015-10-01

    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.

  9. 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

  10. 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

  11. 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

  12. Comparative study on the therapeutic potential of neurally differentiated stem cells in a mouse model of multiple sclerosis.

    Directory of Open Access Journals (Sweden)

    Natalie L Payne

    Full Text Available BACKGROUND: Transplantation of neural stem cells (NSCs is a promising novel approach to the treatment of neuroinflammatory diseases such as multiple sclerosis (MS. NSCs can be derived from primary central nervous system (CNS tissue or obtained by neural differentiation of embryonic stem (ES cells, the latter having the advantage of readily providing an unlimited number of cells for therapeutic purposes. Using a mouse model of MS, we evaluated the therapeutic potential of NSCs derived from ES cells by two different neural differentiation protocols that utilized adherent culture conditions and compared their effect to primary NSCs derived from the subventricular zone (SVZ. METHODOLOGY/PRINCIPAL FINDINGS: The proliferation and secretion of pro-inflammatory cytokines by antigen-stimulated splenocytes was reduced in the presence of SVZ-NSCs, while ES cell-derived NSCs exerted differential immunosuppressive effects. Surprisingly, intravenously injected NSCs displayed no significant therapeutic impact on clinical and pathological disease outcomes in mice with experimental autoimmune encephalomyelitis (EAE induced by recombinant myelin oligodendrocyte glycoprotein, independent of the cell source. Studies tracking the biodistribution of transplanted ES cell-derived NSCs revealed that these cells were unable to traffic to the CNS or peripheral lymphoid tissues, consistent with the lack of cell surface homing molecules. Attenuation of peripheral immune responses could only be achieved through multiple high doses of NSCs administered intraperitoneally, which led to some neuroprotective effects within the CNS. CONCLUSION/SIGNIFICANCE: Systemic transplantation of these NSCs does not have a major influence on the clinical course of rMOG-induced EAE. Improving the efficiency at which NSCs home to inflammatory sites may enhance their therapeutic potential in this model of CNS autoimmunity.

  13. Bcl-2 potentiates the maximal calcium uptake capacity of neural cell mitochondria.

    Science.gov (United States)

    Murphy, A N; Bredesen, D E; Cortopassi, G; Wang, E; Fiskum, G

    1996-01-01

    Expression of the human protooncogene bcl-2 protects neural cells from death induced by many forms of stress, including conditions that greatly elevate intracellular Ca2+. Considering that Bcl-2 is partially localized to mitochondrial membranes and that excessive mitochondrial Ca2+ uptake can impair electron transport and oxidative phosphorylation, the present study tested the hypothesis that mitochondria from Bcl-2-expressing cells have a higher capacity for energy-dependent Ca2+ uptake and a greater resistance to Ca(2+)-induced respiratory injury than mitochondria from cells that do not express this protein. The overexpression of bcl-2 enhanced the mitochondrial Ca2+ uptake capacity using either digitonin-permeabilized GT1-7 neural cells or isolated GT1-7 mitochondria by 1.7 and 3.9 fold, respectively, when glutamate and malate were used as respiratory substrates. This difference was less apparent when respiration was driven by the oxidation of succinate in the presence of the respiratory complex I inhibitor rotenone. Mitochondria from Bcl-2 expressors were also much more resistant to inhibition of NADH-dependent respiration caused by sequestration of large Ca2+ loads. The enhanced ability of mitochondria within Bcl-2-expressing cells to sequester large quantities of Ca2+ without undergoing profound respiratory impairment provides a plausible mechanism by which Bcl-2 inhibits certain forms of delayed cell death, including neuronal death associated with ischemia and excitotoxicity. PMID:8790427

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. Assessing the potential of in-field rainwater harvesting as an adaptation strategy to climate change for African agriculture

    NARCIS (Netherlands)

    Lebel, S.; Fleskens, L.; Forster, P.M.; Jackson, L.S.; Lorenz, S.

    2015-01-01

    Stabilizing smallholder crop yields under changing climatic conditions in sub-Saharan Africa will require adaptation strategies focused on soil and water management. Impact studies of climate change on crop yields often ignore the potential of adaptation strategies such as rainwater harvesting

  19. Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing.

    Science.gov (United States)

    Delisle-Rodriguez, Denis; Villa-Parra, Ana Cecilia; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera-Neto, Anselmo; Krishnan, Sridhar; Rocon, Eduardo

    2017-11-25

    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.

  20. 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.

  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...... specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality (SQ) and speech intelligibility (SI), when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system...

  2. 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.

  3. 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)

  4. Testing a theoretical proposition for modeling and role-modelpan>ing: basic need and adaptive potential status.

    Science.gov (United States)

    Barnfather, J S

    1993-01-01

    The purpose of this research was to test the validity of a propositional statement within the Modeling and Role-Modelpan>ing theory and paradigm (Erickson, Tomlin, & Swain, 1988). The Adaptive Potential Assessment Model (APAM) is part of a multidimensional assessment strategy used by nurses to determine ability to mobilize coping resources. This study tested the theoretical proposition that basic need satisfaction and adaptive potential status are directly related. Three study hypotheses were deduced from the identified theoretical proposition. A simple linear relationship between basic need satisfaction and adaptive potential status was not supported. Multidimensional representations of the relationships contained important findings. Understanding more about the relationships between basic need status and adaptive potential will contribute to refining this theory for use in practice.

  5. 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.

  6. Neural markers for immediate performance accuracy in a Stroop color-word matching task: an event-related potentials analysis.

    Science.gov (United States)

    Shou, Guofa; Ding, Lei

    2014-01-01

    The present study examined the neural markers measured in event-related potentials (ERPs) for immediate performance accuracy during a cognitive task with less conflict, i.e., a Stroop color-word matching task, in which participants were required to judge the congruency of two feature dimensions of a stimulus. In an effort to make ERP components more specific to distinct underlying neural substrates, recorded EEG signals were firstly dissolved into multiple independent components (ICs) using independent component analysis (ICA). Thereafter, individual ICs with prominent sensory- or cognitive-related ERP components were selected to separately reconstruct scalp EEG signals at representative channels, from which ERP waveforms were built, respectively. Statistical comparisons on amplitudes of stimulus-locked ERP components, i.e., prefrontal P2 and N2, parietal P3, bilateral occipital P1 and N1, revealed significant reduced P3 amplitude in error trials than in correct trials. In addition, significant evident ERN was also observed in error trials but not in correct trials. Considering the temporal locus of semantic conflict in the present task, we concluded that reduced P3 amplitude in error trials reflect impaired resolving process of semantic conflict, which further lead to a performance error in the Stroop color-word matching task.

  7. Loss of non-coding RNA expression from the DLK1-DIO3 imprinted locus correlates with reduced neural differentiation potential in human embryonic stem cell lines.

    Science.gov (United States)

    Mo, Chu-Fan; Wu, Fang-Chun; Tai, Kang-Yu; Chang, Wei-Chun; Chang, Kai-Wei; Kuo, Hung-Chih; Ho, Hong-Nerng; Chen, Hsin-Fu; Lin, Shau-Ping

    2015-01-05

    Pluripotent stem cells are increasingly used to build therapeutic models, including the transplantation of neural progenitors derived from human embryonic stem cells (hESCs). Recently, long non-coding RNAs (lncRNAs), including delta-like homolog 1 gene and the type III iodothyronine deiodinase gene (DLK1-DIO3) imprinted locus-derived maternally expressed gene 3 (MEG3), were found to be expressed during neural development. The deregulation of these lncRNAs is associated with various neurological diseases. The imprinted locus DLK1-DIO3 encodes abundant non-coding RNAs (ncRNAs) that are regulated by differential methylation of the locus. We aim to study the correlation between the DLK1-DIO3-derived ncRNAs and the capacity of hESCs to differentiate into neural lineages. We classified hESC sublines into MEG3-ON and MEG3-OFF based on the expression levels of MEG3 and its downstream microRNAs as detected by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). A cDNA microarray was used to analyze the gene expression profiles of hESCs. To investigate the capacity of neural differentiation in MEG3-ON and MEG3-OFF hESCs, we performed neural lineage differentiation followed by neural lineage marker expression and neurite formation analyses via qRT-PCR and immunocytochemistry, respectively. MEG3-knockdown via small interfering RNA (siRNA) and small hairpin RNA (shRNA) was used to investigate the potential causative effect of MEG3 in regulating neural lineage-related gene expression. DLK1-DIO3-derived ncRNAs were repressed in MEG3-OFF hESCs compared with those in the MEG3-ON hESCs. The transcriptome profile indicated that many genes related to nervous system development and neural-type tumors were differentially expressed in MEG3-OFF hESCs. Three independent MEG3-knockdown assays using different siRNA and shRNA constructs consistently resulted in downregulation of some neural lineage genes. Lower expression levels of stage-specific neural lineage markers and

  8. 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...

  9. 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.

  10. The neural and psychological basis of herding in purchasing books online: an event-related potential study.

    Science.gov (United States)

    Chen, Mingliang; Ma, Qingguo; Li, Minle; Dai, Shenyi; Wang, Xiaoyi; Shu, Liangchao

    2010-06-01

    In this study, event-related brain potentials (ERPs) were used to investigate the neural and psychological bases of consumer herding decision in purchasing books online. Sixteen participants were asked to decide as quickly as possible whether to buy a book on the basis of its title keywords and the numbers of positive and negative reviews in stimulus. The given title keywords were very similar, and participants did not have special preference for any particular one. Hence, they were forced to adopt the strategy of herding decision: choosing to buy the book when there were consistent positive reviews, choosing not to buy when there were consistent negative reviews, randomly choosing to buy or not to buy when there were no consistent reviews. The herding decision triggers a categorical processing of the consistency level of customer reviews. Remarkable late positive potential (LPP), a component of ERP sensitive to categorization processes, was elicited. The LPP amplitudes varied as a function of review consistency. The LPP amplitudes for three categories of review consistency were significantly different, and their order is such that absolute consistent review was greater than relative consistent review, which was greater than inconsistent review. In addition, behavioral data revealed that the higher the consistency of the customer reviews, the higher the herd rate. It is possible that customer reviews with higher consistency let participants make herding decisions more resolutely. The present results suggest that the LPP may be regarded as an endogenous neural signal of the herding mechanism in a sense and that the LPP amplitude is potentially a measure of consumers' herd tendency in purchase decisions.

  11. 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.

  12. Inverse relationship of the velocities of perceived time and information processing events in the brain: a potential bioassay for neural functions: a hypothesis.

    Science.gov (United States)

    Rosenberg, R N

    1979-12-01

    The velocity of elapsing time is not a constant but a relativistic component in the space-time continuum as postulated by Albert Einstein in his general and special relativity theories. The hypothesis presented here is that there is a biological corollary to relativity theory. It is postulated that biological time perception is also not a constant but is related by an inverse relationship between the velocities of neural processing events and perceived elapsing time. A careful analysis of this relationship may potentially offer a sensitive bioassay to determine the integrity of regional brain function under normal conditions and in the presence of specific disease processes. The mechanism for the biological basis of this theorem depends on the presence of a neural circuit developed through evolution which monitors overall brain efficiency and is coordinately linked to neural time perceiving circuits. Several test approaches are presented to validate the hypothesis of biologic time relativity compared to the rate of neural processing.

  13. Cells transplanted onto the surface of the glial scar reveal hidden potential for functional neural regeneration.

    Science.gov (United States)

    Sekiya, Tetsuji; Holley, Matthew C; Hashido, Kento; Ono, Kazuya; Shimomura, Koichiro; Horie, Rie T; Hamaguchi, Kiyomi; Yoshida, Atsuhiro; Sakamoto, Tatsunori; Ito, Juichi

    2015-06-30

    Cell transplantation therapy has long been investigated as a therapeutic intervention for neurodegenerative disorders, including spinal cord injury, Parkinson's disease, and amyotrophic lateral sclerosis. Indeed, patients have high hopes for a cell-based therapy. However, there are numerous practical challenges for clinical translation. One major problem is that only very low numbers of donor cells survive and achieve functional integration into the host. Glial scar tissue in chronic neurodegenerative disorders strongly inhibits regeneration, and this inhibition must be overcome to accomplish successful cell transplantation. Intraneural cell transplantation is considered to be the best way to deliver cells to the host. We questioned this view with experiments in vivo on a rat glial scar model of the auditory system. Our results show that intraneural transplantation to the auditory nerve, preceded by chondroitinase ABC (ChABC)-treatment, is ineffective. There is no functional recovery, and almost all transplanted cells die within a few weeks. However, when donor cells are placed on the surface of a ChABC-treated gliotic auditory nerve, they autonomously migrate into it and recapitulate glia- and neuron-guided cell migration modes to repair the auditory pathway and recover auditory function. Surface transplantation may thus pave the way for improved functional integration of donor cells into host tissue, providing a less invasive approach to rescue clinically important neural tracts.

  14. Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe

    Directory of Open Access Journals (Sweden)

    Debaeke Philippe

    2017-01-01

    Full Text Available Climate change is characterized by higher temperatures, elevated atmospheric CO2 concentrations, extreme climatic hazards, and less water available for agriculture. Sunflower, a spring-sown crop often cultivated in southern and eastern regions of Europe, could be more vulnerable to the direct effect of heat stress at anthesis and drought during its growing cycle, both factors resulting in severe yield loss, oil content decrease, and fatty acid alterations. Adaptations through breeding (earliness, stress tolerance, crop management (planting dates, and shifting of growing areas could be developed, assessed and combined to partly cope with these negative impacts. New cultivation opportunities could be expected in northern parts of Europe where sunflower is not grown presently and where it could usefully contribute to diversify cereal-based cropping systems. In addition, sunflower crop could participate to the mitigation solution as a low greenhouse gas emitter compared to cereals and oilseed rape. Sunflower crop models should be revised to account for these emerging environmental factors in order to reduce the uncertainties in yield and oil predictions. The future of sunflower in Europe is probably related to its potential adaptation to climate change but also to its competitiveness and attractiveness for food and energy.

  15. Fisheries-induced neutral and adaptive evolution in exploited fish populations and consequences for their adaptive potential

    DEFF Research Database (Denmark)

    Marty, Lise; Dieckmann, Ulf; Ernande, Bruno

    2015-01-01

    . An individual-based eco-genetic model is devised that includes neutral and functional loci in a realistic ecological setting. In line with theoretical expectations, we find that fishing induces evolution towards slow growth, early maturation at small size and higher reproductive investment. We show, first......Fishing may induce neutral and adaptive evolution affecting life-history traits, and molecular evidence has shown that neutral genetic diversity has declined in some exploited populations. Here, we theoretically study the interplay between neutral and adaptive evolution caused by fishing......, that the choice of genetic model (based on either quantitative genetics or gametic inheritance) influences the evolutionary recovery of traits after fishing ceases. Second, we analyse the influence of three factors possibly involved in the lack of evolutionary recovery: the strength of selection, the effect...

  16. 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.

  17. 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.)

  18. Sustainable Land Management's potential for climate change adaptation in Mediterranean environments: a regional scale assessment

    Science.gov (United States)

    Eekhout, Joris P. C.; de Vente, Joris

    2017-04-01

    Climate change has strong implications for many essential ecosystem services, such as provision of drinking and irrigation water, soil erosion and flood control. Especially large impacts are expected in the Mediterranean, already characterised by frequent floods and droughts. The projected higher frequency of extreme weather events under climate change will lead to an increase of plant water stress, reservoir inflow and sediment yield. Sustainable Land Management (SLM) practices are increasingly promoted as climate change adaptation strategy and to increase resilience against extreme events. However, there is surprisingly little known about their impacts and trade-offs on ecosystem services at regional scales. The aim of this research is to provide insight in the potential of SLM for climate change adaptation, focusing on catchment-scale impacts on soil and water resources. We applied a spatially distributed hydrological model (SPHY), coupled with an erosion model (MUSLE) to the Segura River catchment (15,978 km2) in SE Spain. We run the model for three periods: one reference (1981-2000) and two future scenarios (2031-2050 and 2081-2100). Climate input data for the future scenarios were based on output from 9 Regional Climate Models and for different emission scenarios (RCP 4.5 and RCP 8.5). Realistic scenarios of SLM practices were developed based on a local stakeholder consultation process. The evaluated SLM scenarios focussed on reduced tillage and organic amendments under tree and cereal crops, covering 24% and 15% of the catchment, respectively. In the reference scenario, implementation of SLM at the field-scale led to an increase of the infiltration capacity of the soil and a reduction of surface runoff up to 29%, eventually reducing catchment-scale reservoir inflow by 6%. This led to a reduction of field-scale sediment yield of more than 50% and a reduced catchment-scale sediment flux to reservoirs of 5%. SLM was able to fully mitigate the effect of climate

  19. A neural network potential energy surface for the F + CH4reaction including multiple channels based on coupled cluster theory.

    Science.gov (United States)

    Chen, Jun; Xu, Xin; Liu, Shu; Zhang, Dong H

    2018-03-22

    We report here a new global and full dimensional potential energy surface (PES) for the F + CH4 reaction. This PES was constructed by using neural networks (NN) fitting to about 99 000 ab initio energies computed at the UCCSD(T)-F12a/aug-cc-pVTZ level of theory, and the correction terms considering the influence of a larger basis set as well as spin-orbit couplings were further implemented with a hierarchial scheme. This PES, covering both the abstraction and substitution channels, has an overall fitting error of 8.24 meV in total, and 4.87 meV for energies within 2.5 eV using a segmented NN fitting method, and is more accurate than the previous PESs.

  20. Motor Imagery-Based Rehabilitation: Potential Neural Correlates and Clinical Application for Functional Recovery of Motor Deficits after Stroke.

    Science.gov (United States)

    Tong, Yanna; Pendy, John T; Li, William A; Du, Huishan; Zhang, Tong; Geng, Xiaokun; Ding, Yuchuan

    2017-05-01

    Motor imagery (MI), defined as the mental implementation of an action in the absence of movement or muscle activation, is a rehabilitation technique that offers a means to replace or restore lost motor function in stroke patients when used in conjunction with conventional physiotherapy procedures. This article briefly reviews the concepts and neural correlates of MI in order to promote improved understanding, as well as to enhance the clinical utility of MI-based rehabilitation regimens. We specifically highlight the role of the cerebellum and basal ganglia, premotor, supplementary motor, and prefrontal areas, primary motor cortex, and parietal cortex. Additionally, we examine the recent literature related to MI and its potential as a therapeutic technique in both upper and lower limb stroke rehabilitation.

  1. The adaptive potential of maternal stress exposure in regulating population dynamics.

    Science.gov (United States)

    Sheriff, Michael J

    2015-03-01

    Ecologists, evolutionary biologists and biomedical researchers are investing great effort in understanding the impact maternal stress may have on offspring phenotypes. Bian et al. advance this field by providing evidence that density-induced maternal stress programs offspring phenotypes, resulting in direct consequences on their fitness and population dynamics, but doing so in a context-dependent manner. They suggest that intrinsic state alterations induced by maternal stress may be one ecological factor generating delayed density-dependent effects. This research highlights the connection between maternal stress and population dynamics, and the importance of understanding the adaptive potential of such effects in a context-dependent manner. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

  2. The maternal brain under stress: Consequences for adaptive peripartum plasticity and its potential functional implications.

    Science.gov (United States)

    Slattery, David A; Hillerer, Katharina M

    2016-04-01

    The peripartum period represents a time during which all mammalian species undergo substantial physiological and behavioural changes, which prepare the female for the demands of motherhood. In addition to behavioural and physiological alterations, numerous brain regions, such as the medial prefrontal cortex, olfactory bulb, medial amygdala and hippocampus are subject to substantial peripartum-associated neuronal, dendritic and synaptic plasticity. These changes, which are temporally- and spatially-distinct, are strongly influenced by gonadal and adrenal hormones, such as estrogen and cortisol/corticosterone, which undergo dramatic fluctuations across this period. In this review, we describe our current knowledge regarding these plasticity changes and describe how stress affects such normal adaptations. Finally, we discuss the mechanisms potentially underlying these neuronal, dendritic and synaptic changes and their functional relevance for the mother and her offspring. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. 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

  4. 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

  5. Biotechnological Potential of Cold Adapted Pseudoalteromonas spp. Isolated from ‘Deep Sea’ Sponges

    Directory of Open Access Journals (Sweden)

    Erik Borchert

    2017-06-01

    Full Text Available The marine genus Pseudoalteromonas is known for its versatile biotechnological potential with respect to the production of antimicrobials and enzymes of industrial interest. We have sequenced the genomes of three Pseudoalteromonas sp. strains isolated from different deep sea sponges on the Illumina MiSeq platform. The isolates have been screened for various industrially important enzymes and comparative genomics has been applied to investigate potential relationships between the isolates and their host organisms, while comparing them to free-living Pseudoalteromonas spp. from shallow and deep sea environments. The genomes of the sponge associated Pseudoalteromonas strains contained much lower levels of potential eukaryotic-like proteins which are known to be enriched in symbiotic sponge associated microorganisms, than might be expected for true sponge symbionts. While all the Pseudoalteromonas shared a large distinct subset of genes, nonetheless the number of unique and accessory genes is quite large and defines the pan-genome as open. Enzymatic screens indicate that a vast array of enzyme activities is expressed by the isolates, including β-galactosidase, β-glucosidase, and protease activities. A β-glucosidase gene from one of the Pseudoalteromonas isolates, strain EB27 was heterologously expressed in Escherichia coli and, following biochemical characterization, the recombinant enzyme was found to be cold-adapted, thermolabile, halotolerant, and alkaline active.

  6. Biotechnological Potential of Cold Adapted Pseudoalteromonas spp. Isolated from 'Deep Sea' Sponges.

    Science.gov (United States)

    Borchert, Erik; Knobloch, Stephen; Dwyer, Emilie; Flynn, Sinéad; Jackson, Stephen A; Jóhannsson, Ragnar; Marteinsson, Viggó T; O'Gara, Fergal; Dobson, Alan D W

    2017-06-19

    The marine genus Pseudoalteromonas is known for its versatile biotechnological potential with respect to the production of antimicrobials and enzymes of industrial interest. We have sequenced the genomes of three Pseudoalteromonas sp. strains isolated from different deep sea sponges on the Illumina MiSeq platform. The isolates have been screened for various industrially important enzymes and comparative genomics has been applied to investigate potential relationships between the isolates and their host organisms, while comparing them to free-living Pseudoalteromonas spp. from shallow and deep sea environments. The genomes of the sponge associated Pseudoalteromonas strains contained much lower levels of potential eukaryotic-like proteins which are known to be enriched in symbiotic sponge associated microorganisms, than might be expected for true sponge symbionts. While all the Pseudoalteromonas shared a large distinct subset of genes, nonetheless the number of unique and accessory genes is quite large and defines the pan-genome as open. Enzymatic screens indicate that a vast array of enzyme activities is expressed by the isolates, including β-galactosidase, β-glucosidase, and protease activities. A β-glucosidase gene from one of the Pseudoalteromonas isolates, strain EB27 was heterologously expressed in Escherichia coli and, following biochemical characterization, the recombinant enzyme was found to be cold-adapted, thermolabile, halotolerant, and alkaline active.

  7. Long-term potentiation promotes proliferation/survival and neuronal differentiation of neural stem/progenitor cells.

    Directory of Open Access Journals (Sweden)

    Taesup Cho

    Full Text Available Neural stem cell (NSC replacement therapy is considered a promising cell replacement therapy for various neurodegenerative diseases. However, the low rate of NSC survival and neurogenesis currently limits its clinical potential. Here, we examined if hippocampal long-term potentiation (LTP, one of the most well characterized forms of synaptic plasticity, promotes neurogenesis by facilitating proliferation/survival and neuronal differentiation of NSCs. We found that the induction of hippocampal LTP significantly facilitates proliferation/survival and neuronal differentiation of both endogenous neural progenitor cells (NPCs and exogenously transplanted NSCs in the hippocampus in rats. These effects were eliminated by preventing LTP induction by pharmacological blockade of the N-methyl-D-aspartate glutamate receptor (NMDAR via systemic application of the receptor antagonist, 3-[(R-2-carboxypiperazin-4-yl]-propyl-1-phosphonic acid (CPP. Moreover, using a NPC-neuron co-culture system, we were able to demonstrate that the LTP-promoted NPC neurogenesis is at least in part mediated by a LTP-increased neuronal release of brain-derived neurotrophic factor (BDNF and its consequent activation of tropomysosin receptor kinase B (TrkB receptors on NSCs. Our results indicate that LTP promotes the neurogenesis of both endogenous and exogenously transplanted NSCs in the brain. The study suggests that pre-conditioning of the host brain receiving area with a LTP-inducing deep brain stimulation protocol prior to NSC transplantation may increase the likelihood of success of using NSC transplantation as an effective cell therapy for various neurodegenerative diseases.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Altering second-order configurations reduces the adaptation effects on early face-sensitive event-related potential components

    Directory of Open Access Journals (Sweden)

    Pál eVakli

    2014-06-01

    Full Text Available The spatial distances among the features of a face are commonly referred to as second-order relations, and the coding of these properties is often regarded as a cornerstone in face recognition. Previous studies have provided mixed results regarding whether the N170, a face-sensitive component of the event-related potential, is sensitive to second-order relations. Here we investigated this issue in a gender discrimination paradigm following long-term (5 seconds adaptation to normal or vertically stretched male and female faces, considering that the latter manipulation substantially alters the position of the inner facial features. Gender-ambiguous faces were more likely judged to be female following adaptation to a male face and vice versa. This aftereffect was smaller but statistically significant after being adapted to vertically stretched when compared to unstretched adapters. Event-related potential recordings revealed that adaptation effects measured on the amplitude of the N170 show strong modulations by the second-order relations of the adapter: reduced N170 amplitude was observed, however, this reduction was smaller in magnitude after being adapted to stretched when compared to unstretched faces. These findings suggest early face-processing, as reflected in the N170 component, proceeds by extracting the spatial relations of inner facial features.

  15. 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

  16. 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

    2018-03-05

    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.

  17. 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.

  18. Neurofeedback of slow cortical potentials: neural mechanisms and feasibility of a placebo-controlled design in healthy adults