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Sample records for inhibitory neural system

  1. Asymmetric lateral inhibitory neural activity in the auditory system: a magnetoencephalographic study

    Directory of Open Access Journals (Sweden)

    Gunji Atsuko

    2007-05-01

    Full Text Available Abstract Background Decrements of auditory evoked responses elicited by repeatedly presented sounds with similar frequencies have been well investigated by means of electroencephalography and magnetoencephalography (MEG. However the possible inhibitory interactions between different neuronal populations remains poorly understood. In the present study, we investigated the effect of proceeding notch-filtered noises (NFNs with different frequency spectra on a following test tone using MEG. Results Three-second exposure to the NFNs resulted in significantly different N1m responses to a 1000 Hz test tone presented 500 ms after the offset of the NFNs. The NFN with a lower spectral edge closest to the test tone mostly decreased the N1m amplitude. Conclusion The decrement of the N1m component after exposure to the NFNs could be explained partly in terms of lateral inhibition. The results demonstrated that the amplitude of the N1m was more effectively influenced by inhibitory lateral connections originating from neurons corresponding to lower rather than higher frequencies. We interpret this effect of asymmetric lateral inhibition in the auditory system as an important contribution to reduce the asymmetric neural activity profiles originating from the cochlea.

  2. Death and rebirth of neural activity in sparse inhibitory networks

    OpenAIRE

    Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

    2016-01-01

    In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons' death). However, the random pruning of the connections is able to reverse the action of in...

  3. Decorrelation of Neural-Network Activity by Inhibitory Feedback

    Science.gov (United States)

    Einevoll, Gaute T.; Diesmann, Markus

    2012-01-01

    Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between

  4. Digital implementation of shunting-inhibitory cellular neural network

    Science.gov (United States)

    Hammadou, Tarik; Bouzerdoum, Abdesselam; Bermak, Amine

    2000-05-01

    Shunting inhibition is a model of early visual processing which can provide contrast and edge enhancement, and dynamic range compression. An architecture of digital Shunting Inhibitory Cellular Neural Network for real time image processing is presented. The proposed architecture is intended to be used in a complete vision system for edge detection and image enhancement. The present hardware architecture, is modeled and simulated in VHDL. Simulation results show the functional validity of the proposed architecture.

  5. Delayed excitatory and inhibitory feedback shape neural information transmission

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    Chacron, Maurice J.; Longtin, André; Maler, Leonard

    2017-01-01

    Feedback circuitry with conduction and synaptic delays is ubiquitous in the nervous system. Yet the effects of delayed feedback on sensory processing of natural signals are poorly understood. This study explores the consequences of delayed excitatory and inhibitory feedback inputs on the processing of sensory information. We show, through numerical simulations and theory, that excitatory and inhibitory feedback can alter the firing frequency response of stochastic neurons in opposite ways by creating dynamical resonances, which in turn lead to information resonances (i.e., increased information transfer for specific ranges of input frequencies). The resonances are created at the expense of decreased information transfer in other frequency ranges. Using linear response theory for stochastically firing neurons, we explain how feedback signals shape the neural transfer function for a single neuron as a function of network size. We also find that balanced excitatory and inhibitory feedback can further enhance information tuning while maintaining a constant mean firing rate. Finally, we apply this theory to in vivo experimental data from weakly electric fish in which the feedback loop can be opened. We show that it qualitatively predicts the observed effects of inhibitory feedback. Our study of feedback excitation and inhibition reveals a possible mechanism by which optimal processing may be achieved over selected frequency ranges. PMID:16383655

  6. Neural correlation is stimulus modulated by feedforward inhibitory circuitry.

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    Middleton, Jason W; Omar, Cyrus; Doiron, Brent; Simons, Daniel J

    2012-01-11

    Correlated variability of neural spiking activity has important consequences for signal processing. How incoming sensory signals shape correlations of population responses remains unclear. Cross-correlations between spiking of different neurons may be particularly consequential in sparsely firing neural populations such as those found in layer 2/3 of sensory cortex. In rat whisker barrel cortex, we found that pairs of excitatory layer 2/3 neurons exhibit similarly low levels of spike count correlation during both spontaneous and sensory-evoked states. The spontaneous activity of excitatory-inhibitory neuron pairs is positively correlated, while sensory stimuli actively decorrelate joint responses. Computational modeling shows how threshold nonlinearities and local inhibition form the basis of a general decorrelating mechanism. We show that inhibitory population activity maintains low correlations in excitatory populations, especially during periods of sensory-evoked coactivation. The role of feedforward inhibition has been previously described in the context of trial-averaged phenomena. Our findings reveal a novel role for inhibition to shape correlations of neural variability and thereby prevent excessive correlations in the face of feedforward sensory-evoked activation.

  7. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  8. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  9. An analysis of inhibitory pseudo-interconnections in unsupervised neural networks

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    Tran, Minh-Triet; Le, Nam Do-Hoang

    2013-12-01

    Lateral connection is a fundamental element of human neural networks which enables sparse learning and topographical order in feature maps. Due to high complexity and computational cost, computer scientists tend to simplify it in practical implementations. To utilize the simplicity of traditional networks while preserving the effects of interconnections, the authors employ numerical filters in unsupervised learning networks. These filters suppress low activations and decorrelate high ones, which are similar to how inhibitory lateral connections behave. Inhibitory networks outperform conventional approach in both standard datasets CIFAR-10 and STL-10. Our method also yields competitive results in comparison with other single-layer unsupervised networks. Furthermore, it is promising to apply inhibitory networks into deep learning systems for complex recognition problem.

  10. Reciprocal inhibitory connections within a neural network for rotational optic-flow processing

    Directory of Open Access Journals (Sweden)

    Juergen Haag

    2007-10-01

    Full Text Available Neurons in the visual system of the blowfly have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow-field selectivity in proximal Vertical System (VS-cells, a particular subset of tangential cells in the fly. These cells have local preferred directions that are distributed such as to match the flow field occurring during a rotation of the fly. However, the neural circuitry leading to this selectivity is not fully understood. Through dual intracellular recordings from proximal VS cells and other tangential cells, we characterized the specific wiring between VS cells themselves and between proximal VS cells and horizontal sensitive tangential cells. We discovered a spiking neuron (Vi involved in this circuitry that has not been described before. This neuron turned out to be connected to proximal VS cells via gap junctions and, in addition, it was found to be inhibitory onto VS1.

  11. Multiplicative Inhibitory Velocity Detector and Multi-Velocity Motion Detection Neural Network Model

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    Motion perception is one of the most important aspects of the biological visual system,from which people get a lot of information of the natural world.In this paper,trying to simulate the neurons in MT(motion area in visual cortex)which respond selectively both in direction and speed,the authors propose a novel multiplicative inhibitory velocity detector(MIVD)model,whose spatiotemporal joint parameter K determines its optimal velocity.Based on the Response Amplitude Disparity(RAD) property of MIVD,two multi-velocity fusion neural networks(a simple one and an active one)are built to detect the velocity of 1-Dimension motion.The experiments show that the active MIVD Neural Network with a feedback fusion method has a relatively better result.

  12. Convergence of inhibitory neural inputs regulate motor activity in the murine and monkey stomach.

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    Shaylor, Lara A; Hwang, Sung Jin; Sanders, Kenton M; Ward, Sean M

    2016-11-01

    Inhibitory motor neurons regulate several gastric motility patterns including receptive relaxation, gastric peristaltic motor patterns, and pyloric sphincter opening. Nitric oxide (NO) and purines have been identified as likely candidates that mediate inhibitory neural responses. However, the contribution from each neurotransmitter has received little attention in the distal stomach. The aims of this study were to identify the roles played by NO and purines in inhibitory motor responses in the antrums of mice and monkeys. By using wild-type mice and mutants with genetically deleted neural nitric oxide synthase (Nos1(-/-)) and P2Y1 receptors (P2ry1(-/-)) we examined the roles of NO and purines in postjunctional inhibitory responses in the distal stomach and compared these responses to those in primate stomach. Activation of inhibitory motor nerves using electrical field stimulation (EFS) produced frequency-dependent inhibitory junction potentials (IJPs) that produced muscle relaxations in both species. Stimulation of inhibitory nerves during slow waves terminated pacemaker events and associated contractions. In Nos1(-/-) mice IJPs and relaxations persisted whereas in P2ry1(-/-) mice IJPs were absent but relaxations persisted. In the gastric antrum of the non-human primate model Macaca fascicularis, similar NO and purine neural components contributed to inhibition of gastric motor activity. These data support a role of convergent inhibitory neural responses in the regulation of gastric motor activity across diverse species.

  13. Neural signal transduction aided by noise in multisynaptic excitatory and inhibitory pathways with saturation

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    Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

    2011-08-01

    We study the stochastic resonance phenomenon in saturating dynamical models of neural signal transduction, at the synaptic stage, wherein the noise in multipathways enhances the processing of neuronal information integrated by excitatory and inhibitory synaptic currents. For an excitatory synaptic pathway, the additive intervention of an inhibitory pathway reduces the stochastic resonance effect. However, as the number of synaptic pathways increases, the signal transduction is greatly improved for parallel multipathways that feature both excitation and inhibition. The obtained results lead us to the realization that the collective property of inhibitory synapses assists neural signal transmission, and a parallel array of neurons can enhance their responses to multiple synaptic currents by adjusting the contributions of excitatory and inhibitory currents.

  14. Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

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    Bertolotti, Elena; Burioni, Raffaella; di Volo, Matteo; Vezzani, Alessandro

    2017-01-01

    We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons fI and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on fI, highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

  15. Almost periodic solution of shunting inhibitory cellular neural networks with time-varying delay

    Energy Technology Data Exchange (ETDEWEB)

    Huang Xia; Cao Jinde

    2003-07-28

    Several sufficient conditions are obtained for the existence of almost periodic solution and its attractivity of shunting inhibitory cellular neural networks with time-varying delay based on the fixed point method and Halanay inequality technique. Some previous results are improved and extended in this Letter and two examples are given to illustrate the effectiveness of the new results.

  16. Inhibitory neural pathway regulating gastric emptying in rats.

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    Ishiguchi, T; Nishioka, S; Takahashi, T

    2000-02-14

    The relaxation of the pylorus is one of the most important factors for promoting gastric emptying. However, the role of inhibitory neurotransmitters in the regulation of pyloric relaxation and gastric emptying remains unclear. In this study, we investigated the effects of NO biosynthesis inhibitor, N(G)-nitro-L-arginine methyl ester (L-NAME), and calcium dependent potassium channel blocker, apamin, on vagal stimulation-induced pyloric relaxation and gastric emptying in rats. Sodium nitroprusside (SNP), adenosine 5'-triphosphate (ATP), vasoactive intestinal polypeptide (VIP) and pituitary adenylate cyclase-activating peptide (PACAP) caused pyloric relaxations in a dose dependent manner in vivo. Apamin (120 microg/kg) significantly reduced ATP and PACAP-induced pyloric relaxations without affecting SNP- or VIP-induced relaxations. Vagal stimulation (10 V, 1 ms, 1-20 Hz)-induced pyloric relaxation was significantly inhibited by L-NAME (10 mg/kg). The combined administration of L-NAME and apamin almost completely abolished vagal stimulation-induced pyloric relaxation. L-NAME and apamin significantly increased spontaneous contractions in the antrum, pylorus and duodenum. Increased motility index by L-NAME and apamin was significantly higher in the pylorus and duodenum, compared to that of antrum. L-NAME and apamin significantly delayed liquid gastric emptying. These results suggest that besides NO, probably ATP and PACAP, act as inhibitory neurotransmitters in the rat pylorus and regulate gastric emptying.

  17. The neural basis of inhibitory effects of semantic and phonological neighbors in spoken word production.

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    Mirman, Daniel; Graziano, Kristen M

    2013-09-01

    Theories of word production and word recognition generally agree that multiple word candidates are activated during processing. The facilitative and inhibitory effects of these "lexical neighbors" have been studied extensively using behavioral methods and have spurred theoretical development in psycholinguistics, but relatively little is known about the neural basis of these effects and how lesions may affect them. This study used voxel-wise lesion overlap subtraction to examine semantic and phonological neighbor effects in spoken word production following left hemisphere stroke. Increased inhibitory effects of near semantic neighbors were associated with inferior frontal lobe lesions, suggesting impaired selection among strongly activated semantically related candidates. Increased inhibitory effects of phonological neighbors were associated with posterior superior temporal and inferior parietal lobe lesions. In combination with previous studies, these results suggest that such lesions cause phonological-to-lexical feedback to more strongly activate phonologically related lexical candidates. The comparison of semantic and phonological neighbor effects and how they are affected by left hemisphere lesions provides new insights into the cognitive dynamics and neural basis of phonological, semantic, and cognitive control processes in spoken word production.

  18. Artificial Neural Network Analysis System

    Science.gov (United States)

    2007-11-02

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  19. New Results on Almost Periodic Solution of Shunting Inhibitory Cellular Neural Networks with Continuously Distributed Delays

    Institute of Scientific and Technical Information of China (English)

    Jing Liu; Pei-Yong Zhu

    2008-01-01

    In this paper, the existence, uniqueness and global attractivity are discussed on almost periodic solution of SICNNs (shunting inhibitory cellular neural networks) with continuously distributed delays. By using the fixed point theorem, differential inequality technique and Lyapunov functional method, giving the new ranges of parameters, several sufficient conditions are obtained to ensure the existence, uniqueness and global attractivity of almost periodic solution. Compared with the previous studies, our methods are more effective for almost periodic solution analysis of SICNNs with continuously distributed delays. Some existing results have been improved and extended. In order to show the effectiveness of the obtained results, an example is given in this paper.

  20. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  1. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2016-02-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns

  2. Traveling waves and breathers in an excitatory-inhibitory neural field

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    Folias, Stefanos E.

    2017-03-01

    We study existence and stability of traveling activity bump solutions in an excitatory-inhibitory (E-I) neural field with Heaviside firing rate functions by deriving existence conditions for traveling bumps and an Evans function to analyze their spectral stability. Subsequently, we show that these existence and stability results reduce, in the limit of wave speed c →0 , to the equivalent conditions developed for the stationary bump case. Using the results for the stationary bump case, we show that drift bifurcations of stationary bumps serve as a mechanism for generating traveling bump solutions in the E-I neural field as parameters are varied. Furthermore, we explore the interrelations between stationary and traveling types of bumps and breathers (time-periodic oscillatory bumps) by bridging together analytical and simulation results for stationary and traveling bumps and their bifurcations in a region of parameter space. Interestingly, we find evidence for a codimension-2 drift-Hopf bifurcation occurring as two parameters, inhibitory time constant τ and I-to-I synaptic connection strength w¯i i, are varied and show that the codimension-2 point serves as an organizing center for the dynamics of these four types of spatially localized solutions. Additionally, we describe a case involving subcritical bifurcations that lead to traveling waves and breathers as τ is varied.

  3. The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

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    Yi Qi

    2014-01-01

    Full Text Available We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution.

  4. A potential inhibitory function of draxin in regulating mouse trunk neural crest migration.

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    Zhang, Sanbing; Su, Yuhong; Gao, Jinbao; Zhang, Chenbing; Tanaka, Hideaki

    2017-01-01

    Draxin is a repulsive axon guidance protein that plays important roles in the formation of three commissures in the central nervous system and dorsal interneuron 3 (dI3) in the chick spinal cord. In the present study, we report the expression pattern of mouse draxin in the embryonic mouse trunk spinal cord. In the presence of draxin, the longest net migration length of a migrating mouse trunk neural crest cell was significantly reduced. In addition, the relative number of apolar neural crest cells increased as the draxin treatment time increased. Draxin caused actin cytoskeleton rearrangement in the migrating trunk neural crest cells. Our data suggest that draxin may regulate mouse trunk neural crest cell migration by the rearrangement of cell actin cytoskeleton and by reducing the polarization activity of these cells subsequently.

  5. Memory Storage and Neural Systems.

    Science.gov (United States)

    Alkon, Daniel L.

    1989-01-01

    Investigates memory storage and molecular nature of associative-memory formation by analyzing Pavlovian conditioning in marine snails and rabbits. Presented is the design of a computer-based memory system (neural networks) using the rules acquired in the investigation. Reports that the artificial network recognized patterns well. (YP)

  6. The effects of inhibitory control training for preschoolers on reasoning ability and neural activity.

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    Liu, Qian; Zhu, Xinyi; Ziegler, Albert; Shi, Jiannong

    2015-09-23

    Inhibitory control (including response inhibition and interference control) develops rapidly during the preschool period and is important for early cognitive development. This study aimed to determine the training and transfer effects on response inhibition in young children. Children in the training group (N = 20; 12 boys, mean age 4.87 ± 0.26 years) played "Fruit Ninja" on a tablet computer for 15 min/day, 4 days/week, for 3 weeks. Children in the active control group (N = 20; 10 boys, mean age 4.88 ± 0.20 years) played a coloring game on a tablet computer for 10 min/day, 1-2 days/week, for 3 weeks. Several cognitive tasks (involving inhibitory control, working memory, and fluid intelligence) were used to evaluate the transfer effects, and electroencephalography (EEG) was performed during a go/no-go task. Progress on the trained game was significant, while performance on a reasoning task (Raven's Progressive Matrices) revealed a trend-level improvement from pre- to post-test. EEG indicated that the N2 effect of the go/no-go task was enhanced after training for girls. This study is the first to show that pure response inhibition training can potentially improve reasoning ability. Furthermore, gender differences in the training-induced changes in neural activity were found in preschoolers.

  7. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    Science.gov (United States)

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

  8. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  9. Global Exponential Stability of Almost Periodic Solution for Neutral-Type Cohen-Grossberg Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Impulses.

    Science.gov (United States)

    Xu, Lijun; Jiang, Qi; Gu, Guodong

    2016-01-01

    A kind of neutral-type Cohen-Grossberg shunting inhibitory cellular neural networks with distributed delays and impulses is considered. Firstly, by using the theory of impulsive differential equations and the contracting mapping principle, the existence and uniqueness of the almost periodic solution for the above system are obtained. Secondly, by constructing a suitable Lyapunov functional, the global exponential stability of the unique almost periodic solution is also investigated. The work in this paper improves and extends some results in recent years. As an application, an example and numerical simulations are presented to demonstrate the feasibility and effectiveness of the main results.

  10. Global Exponential Stability of Almost Periodic Solution for Neutral-Type Cohen-Grossberg Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Impulses

    Directory of Open Access Journals (Sweden)

    Lijun Xu

    2016-01-01

    Full Text Available A kind of neutral-type Cohen-Grossberg shunting inhibitory cellular neural networks with distributed delays and impulses is considered. Firstly, by using the theory of impulsive differential equations and the contracting mapping principle, the existence and uniqueness of the almost periodic solution for the above system are obtained. Secondly, by constructing a suitable Lyapunov functional, the global exponential stability of the unique almost periodic solution is also investigated. The work in this paper improves and extends some results in recent years. As an application, an example and numerical simulations are presented to demonstrate the feasibility and effectiveness of the main results.

  11. Fuzzy logic systems are equivalent to feedforward neural networks

    Institute of Scientific and Technical Information of China (English)

    李洪兴

    2000-01-01

    Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.

  12. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  13. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  14. Nonlinear System Control Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Jaroslava Žilková

    2006-10-01

    Full Text Available The paper is focused especially on presenting possibilities of applying off-linetrained artificial neural networks at creating the system inverse models that are used atdesigning control algorithm for non-linear dynamic system. The ability of cascadefeedforward neural networks to model arbitrary non-linear functions and their inverses isexploited. This paper presents a quasi-inverse neural model, which works as a speedcontroller of an induction motor. The neural speed controller consists of two cascadefeedforward neural networks subsystems. The first subsystem provides desired statorcurrent components for control algorithm and the second subsystem providescorresponding voltage components for PWM converter. The availability of the proposedcontroller is verified through the MATLAB simulation. The effectiveness of the controller isdemonstrated for different operating conditions of the drive system.

  15. Optogenetics Comes of Age: Novel Inhibitory Light-Gated Anionic Channels Allow Efficient Silencing of Neural Function.

    Science.gov (United States)

    Peralvárez-Marín, Alex; Garriga, Pere

    2016-02-01

    Optogenetics, the developing field of research that uses light-switchable biochemical tools in a sophisticated technological approach to monitor or control neural function, is rapidly evolving with the discovery and development of novel microbial rhodopsins. Light-absorbing membrane proteins, as tools for brain research, are promoting new applications within the discipline of optogenetics. Light-gated rhodopsin ion channels with better intrinsic light sensitivity and improved resolution are needed to overcome some of the current limitations of existing molecules. The recent discovery of light-gated inhibitory anion channels opens new opportunities for studying physiological neural processes and, at the same time, represent a powerful approach for elucidating the mechanisms of neurological and mental disorders that could benefit from this approach.

  16. Digital systems for artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Atlas, L.E. (Interactive Systems Design Lab., Univ. of Washington, WA (US)); Suzuki, Y. (NTT Human Interface Labs. (US))

    1989-11-01

    A tremendous flurry of research activity has developed around artificial neural systems. These systems have also been tested in many applications, often with positive results. Most of this work has taken place as digital simulations on general-purpose serial or parallel digital computers. Specialized neural network emulation systems have also been developed for more efficient learning and use. The authors discussed how dedicated digital VLSI integrated circuits offer the highest near-term future potential for this technology.

  17. Bistability Analysis of Excitatory-Inhibitory Neural Networks in Limited-Sustained-Activity Regime

    Institute of Scientific and Technical Information of China (English)

    倪赟; 吴亮; 吴丹; 朱士群

    2011-01-01

    Bistable behavior of neuronal complex networks is investigated in the limited-sustained-activity regime when the network is composed of excitatory and inhibitory neurons. The standard stability analysis is performed on the two metastable states separately. Both theoretical analysis and numerical simulations show consistently that the difference between time scales of excitatory and inhibitory populations can influence the dynamical behaviors of the neuronal networks dramatically, leading to the transition from bistable behaviors with memory effects to the collapse of bistable behaviors. These results may suggest one possible neuronal information processing by only tuning time scales.

  18. Neural mechanisms of proactive and reactive inhibitory control : Studies in healthy volunteers and schizophrenia patients

    NARCIS (Netherlands)

    Zandbelt, B.B.

    2011-01-01

    The neural underpinnings of our ability to restrain actions in advance (i.e. proactive inhibition) and stop actions in reaction to some event (i.e. reactive inhibition) remain largely unknown. In this thesis we used neuroimaging (functional magnetic resonance imaging, fMRI) and brain stimulation (tr

  19. The quantum human central neural system.

    Science.gov (United States)

    Alexiou, Athanasios; Rekkas, John

    2015-01-01

    In this chapter we present Excess Entropy Production for human aging system as the sum of their respective subsystems and electrophysiological status. Additionally, we support the hypothesis of human brain and central neural system quantumness and we strongly suggest the theoretical and philosophical status of human brain as one of the unknown natural Dirac magnetic monopoles placed in the center of a Riemann sphere.

  20. Some Applications of Spiking Neural P Systems

    OpenAIRE

    Mihai Ionescu; Dragoş Sburlan

    2012-01-01

    In this paper we investigate some applications of spiking neural P systems regarding their capability to solve some classical computer science problems. In this respect versatility of such systems is studied to simulate a well known parallel computational model, namely the Boolean circuits. In addition, another notorious application -- sorting -- is considered within this framework.

  1. Neural Mechanisms of Inhibitory Response in a Battlefield Scenario: A Simultaneous fMRI-EEG Study

    Science.gov (United States)

    Ko, Li-Wei; Shih, Yi-Cheng; Chikara, Rupesh Kumar; Chuang, Ya-Ting; Chang, Erik C.

    2016-01-01

    The stop-signal paradigm has been widely adopted as a way to parametrically quantify the response inhibition process. To evaluate inhibitory function in realistic environmental settings, the current study compared stop-signal responses in two different scenarios: one uses simple visual symbols as go and stop signals, and the other translates the typical design into a battlefield scenario (BFS) where a sniper-scope view was the background, a terrorist image was the go signal, a hostage image was the stop signal, and the task instructions were to shoot at terrorists only when hostages were not present but to refrain from shooting if hostages appeared. The BFS created a threatening environment and allowed the evaluation of how participants’ inhibitory control manifest in this realistic stop-signal task. In order to investigate the participants’ brain activities with both high spatial and temporal resolution, simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) recordings were acquired. The results demonstrated that both scenarios induced increased activity in the right inferior frontal gyrus (rIFG) and presupplementary motor area (preSMA), which have been linked to response inhibition. Notably, in right temporoparietal junction (rTPJ) we found both higher blood-oxygen-level dependent (BOLD) activation and synchronization of theta-alpha activities (4–12 Hz) in the BFS than in the traditional scenario after the stop signal. The higher activation of rTPJ in the BFS may be related to morality judgments or attentional reorienting. These results provided new insights into the complex brain networks involved in inhibitory control within naturalistic environments. PMID:27199708

  2. The effects of inhibitory control training for preschoolers on reasoning ability and neural activity

    DEFF Research Database (Denmark)

    Liu, Qian; Zhu, Xinyi; Ziegler, Albert

    2015-01-01

    /week, for 3 weeks. Several cognitive tasks (involving inhibitory control, working memory, and fluid intelligence) were used to evaluate the transfer effects, and electroencephalography (EEG) was performed during a go/no-go task. Progress on the trained game was significant, while performance on a reasoning...... task (Raven’s Progressive Matrices) revealed a trend-level improvement from pre- to post-test. EEG indicated that the N2 effect of the go/no-go task was enhanced after training for girls. This study is the first to show that pure response inhibition training can potentially improve reasoning ability...

  3. Estimating neural background input with controlled and fast perturbations: A bandwidth comparison between inhibitory opsins and neural circuits

    Directory of Open Access Journals (Sweden)

    David Eriksson

    2016-08-01

    Full Text Available To test the importance of a certain cell type or brain area it is common to make a lack of function experiment in which the neuronal population of interest is inhibited. Here we review physiological and methodological constraints for making controlled perturbations using the corticothalamic circuit as an example. The brain with its many types of cells and rich interconnectivity offers many paths through which a perturbation can spread within a short time. To understand the side effects of the perturbation one should record from those paths. We find that ephaptic effects, gap-junctions, and fast chemical synapses are so fast that they can react to the perturbation during the few milliseconds it takes for an opsin to change the membrane potential. The slow chemical synapses, astrocytes, extracellular ions and vascular signals, will continue to give their physiological input for around 20 milliseconds before they also react to the perturbation. Although we show that some pathways can react within milliseconds the strength/speed reported in this review should be seen as an upper bound since we have omitted how polysynaptic signals are attenuated. Thus the number of additional recordings that has to be made to control for the perturbation side effects is expected to be fewer than proposed here. To summarize, the reviewed literature not only suggests that it is possible to make controlled lack of function experiments, but, it also suggests that such a lack of function experiment can be used to measure the context of local neural computations.

  4. Leukemia inhibitory factor (LIF) enhances MAP2 + and HUC/D + neurons and influences neurite extension during differentiation of neural progenitors derived from human embryonic stem cells.

    Science.gov (United States)

    Leukemia Inhibitory Factor (L1F), a member of the Interleukin 6 cytokine family, has a role in differentiation of Human Neural Progenitor (hNP) cells in vitro. hNP cells, derived from Human Embryonic Stem (hES) cells, have an unlimited capacity for self-renewal in monolayer cultu...

  5. Leukemia inhibitory factor (LIF) enhances MAP2 + and HUC/D + neurons and influences neurite extension during differentiation of neural progenitors derived from human embryonic stem cells.

    Science.gov (United States)

    Leukemia Inhibitory Factor (L1F), a member of the Interleukin 6 cytokine family, has a role in differentiation of Human Neural Progenitor (hNP) cells in vitro. hNP cells, derived from Human Embryonic Stem (hES) cells, have an unlimited capacity for self-renewal in monolayer cultu...

  6. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Kannada character recognition system using neural network

    Science.gov (United States)

    Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

    2013-03-01

    Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

  8. Silicon synaptic transistor for hardware-based spiking neural network and neuromorphic system

    Science.gov (United States)

    Kim, Hyungjin; Hwang, Sungmin; Park, Jungjin; Park, Byung-Gook

    2017-10-01

    Brain-inspired neuromorphic systems have attracted much attention as new computing paradigms for power-efficient computation. Here, we report a silicon synaptic transistor with two electrically independent gates to realize a hardware-based neural network system without any switching components. The spike-timing dependent plasticity characteristics of the synaptic devices are measured and analyzed. With the help of the device model based on the measured data, the pattern recognition capability of the hardware-based spiking neural network systems is demonstrated using the modified national institute of standards and technology handwritten dataset. By comparing systems with and without inhibitory synapse part, it is confirmed that the inhibitory synapse part is an essential element in obtaining effective and high pattern classification capability.

  9. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  10. Neural circuits as computational dynamical systems.

    Science.gov (United States)

    Sussillo, David

    2014-04-01

    Many recent studies of neurons recorded from cortex reveal complex temporal dynamics. How such dynamics embody the computations that ultimately lead to behavior remains a mystery. Approaching this issue requires developing plausible hypotheses couched in terms of neural dynamics. A tool ideally suited to aid in this question is the recurrent neural network (RNN). RNNs straddle the fields of nonlinear dynamical systems and machine learning and have recently seen great advances in both theory and application. I summarize recent theoretical and technological advances and highlight an example of how RNNs helped to explain perplexing high-dimensional neurophysiological data in the prefrontal cortex.

  11. Spiking neural P systems with weights.

    Science.gov (United States)

    Wang, Jun; Hoogeboom, Hendrik Jan; Pan, Linqiang; Păun, Gheorghe; Pérez-Jiménez, Mario J

    2010-10-01

    A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.

  12. The labeled systems of multiple neural networks.

    Science.gov (United States)

    Nemissi, M; Seridi, H; Akdag, H

    2008-08-01

    This paper proposes an implementation scheme of K-class classification problem using systems of multiple neural networks. Usually, a multi-class problem is decomposed into simple sub-problems solved independently using similar single neural networks. For the reason that these sub-problems are not equivalent in their complexity, we propose a system that includes reinforced networks destined to solve complicated parts of the entire problem. Our approach is inspired from principles of the multi-classifiers systems and the labeled classification, which aims to improve performances of the networks trained by the Back-Propagation algorithm. We propose two implementation schemes based on both OAO (one-against-all) and OAA (one-against-one). The proposed models are evaluated using iris and human thigh databases.

  13. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  14. Simulating neural systems with Xyce.

    Energy Technology Data Exchange (ETDEWEB)

    Schiek, Richard Louis; Thornquist, Heidi K.; Mei, Ting; Warrender, Christina E.; Aimone, James Bradley; Teeter, Corinne; Duda, Alex M.

    2012-12-01

    Sandias parallel circuit simulator, Xyce, can address large scale neuron simulations in a new way extending the range within which one can perform high-fidelity, multi-compartment neuron simulations. This report documents the implementation of neuron devices in Xyce, their use in simulation and analysis of neuron systems.

  15. Hopfield neural network based on ant system

    Institute of Scientific and Technical Information of China (English)

    洪炳镕; 金飞虎; 郭琦

    2004-01-01

    Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

  16. Institute for Brain and Neural Systems

    Science.gov (United States)

    2009-10-06

    Cooper. A Probabilistic Model For Cursive Handwriting Recognition Using Spatial Context. ICASSP, Vol. 5, pp. 201-204, 2005. Technical Reports: 47 21...application to recognition of on-line cursive script. In Advances in Neural Information Processing Systems. Neskovic, P., Schuster, D., and Cooper, L. (2004...P., and Cooper, L. (2005c). A probabilistic model for cursive handwriting recognition using spatial context. In Proc. ICASSP. Wang, J., Neskovic, P

  17. On minimal inhibitory rules for almost all k-valued information systems

    KAUST Repository

    Moshkov, Mikhail

    2009-07-30

    The minimal inhibitory rules for information systems can be used for construction of classifiers. We show that almost all information systems from a certain large class of information systems have relatively short minimal inhibitory rules. However, the number of such rules is not polynomial in the number of attributes and the number of objects. This class consists of all k-valued information systems, k ≥ 2, with the number of objects polynomial in the number of attributes. Hence, for efficient construction of classifiers some filtration techniques in rule generation are necessary. Another way is to work with lazy classification algorithms based on inhibitory rules.

  18. The endocannabinoid system drives neural progenitor proliferation.

    Science.gov (United States)

    Aguado, Tania; Monory, Krisztina; Palazuelos, Javier; Stella, Nephi; Cravatt, Benjamin; Lutz, Beat; Marsicano, Giovanni; Kokaia, Zaal; Guzmán, Manuel; Galve-Roperh, Ismael

    2005-10-01

    The discovery of multipotent neural progenitor (NP) cells has provided strong support for the existence of neurogenesis in the adult brain. However, the signals controlling NP proliferation remain elusive. Endocannabinoids, the endogenous counterparts of marijuana-derived cannabinoids, act as neuromodulators via presynaptic CB1 receptors and also control neural cell death and survival. Here we show that progenitor cells express a functional endocannabinoid system that actively regulates cell proliferation both in vitro and in vivo. Specifically, NPs produce endocannabinoids and express the CB1 receptor and the endocannabinoid-inactivating enzyme fatty acid amide hydrolase (FAAH). CB1 receptor activation promotes cell proliferation and neurosphere generation, an action that is abrogated in CB1-deficient NPs. Accordingly, proliferation of hippocampal NPs is increased in FAAH-deficient mice. Our results demonstrate that endocannabinoids constitute a new group of signaling cues that regulate NP proliferation and thus open novel therapeutic avenues for manipulation of NP cell fate in the adult brain.

  19. Dynamic artificial neural networks with affective systems.

    Science.gov (United States)

    Schuman, Catherine D; Birdwell, J Douglas

    2013-01-01

    Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system can control the firing rate of the ensemble of neurons in the ANN, as well as to explore the coupling between the affective system and the processes of long term potentiation (LTP) and long term depression (LTD), and the effect of the parameters of the affective system on its performance. We apply our networks with affective systems to a simple pole balancing example and briefly discuss the effect of affective systems on network performance.

  20. Dynamical systems, attractors, and neural circuits.

    Science.gov (United States)

    Miller, Paul

    2016-01-01

    Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

  1. Cycles of insanity and creativity within contemplative neural systems.

    Science.gov (United States)

    Thaler, Stephen L

    2016-09-01

    Random connection weight disturbances within an assembly of artificial neural networks (ANN) drive a progression of activation patterns that are tantamount to the memories and ideas nucleating within the brain's cortex. The numerical evaluation of these pattern-based notions by another, more placid system of ANNs governs the magnitude of weight disturbances administered to the former assembly, that perturbative intensity in turn controlling the novelty of the resulting ideational stream as well as the retention of newly formed concepts. In search of solution patterns to posed problems, such collaborating neural systems autonomously cycle between two extremes in mean synaptic perturbation level. The higher limit, characterized by chaos and inattentiveness to exogenous input patterns, is the regime in which ideas first form and incubate. The lower bound, marked by relative synaptic tranquility, is favorable to the reactivation and reinforcement of concepts first seeded during heightened perturbation. When considering this synthetic neural architecture as a cognitive model, the proposed source of such synaptic fluctuations is volume neurotransmitter release within cortex where both ideational and critic nets are commingled. As a result of their overlap, not only are the generative cortical networks suffused with neurotransmitters, but also those functioning in a critic role, leading to altered 'opinions' about the perturbation-driven stream of consciousness that then govern the injection of neurotransmitters into cortex. The likely effect of such chemical feedback is that the brain constantly cycles between states of idea generating chaos and perception stabilizing tranquility in much the same way that creative artificial neural systems do. Postulating that ideas are potentially useful or interesting false memories born within such turmoil, creativity appears to take place through a cyclic process consisting of alternating phases of (1) cognitive incapacitation

  2. Nonlinear system identification and control based on modular neural networks.

    Science.gov (United States)

    Puscasu, Gheorghe; Codres, Bogdan

    2011-08-01

    A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

  3. Research and Design of a Fuzzy Neural Expert System

    Institute of Scientific and Technical Information of China (English)

    王仕军; 王树林

    1995-01-01

    We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.

  4. Microglia-derived interleukin-6 and leukaemia inhibitory factor promote astrocytic differentiation of neural stem/progenitor cells.

    Science.gov (United States)

    Nakanishi, Masaya; Niidome, Tetsuhiro; Matsuda, Satoru; Akaike, Akinori; Kihara, Takeshi; Sugimoto, Hachiro

    2007-02-01

    Neural stem/progenitor cells (NSPCs) proliferate and differentiate depending on their intrinsic properties and local environment. It has been recognized that astrocytes promote neurogenic differentiation of NSPCs, suggesting the importance of cell-cell interactions between glial cells and NSPCs. Recent studies have demonstrated that microglia, one type of glial cells, play an important role in neurogenesis. However, little is known about how activated microglia control the proliferation and differentiation of NSPCs. In this study, we investigated the possibility that microglia-derived soluble factors regulate the behaviour of NSPCs. To this end, NSPCs and microglial cultures were obtained from rat embryonic day 16 subventricular zone (SVZ) and rat postnatal 1 day cortex, respectively, and the conditioned medium from microglia was prepared. Microglial-conditioned medium had no significant effect on the proliferation of NSPCs. In contrast, it increased the percentage of cells positive for a marker of astrocytes, glial fibrillary acidic protein (GFAP) during differentiation. The induction of astrocytic differentiation by microglial-conditioned medium was reduced by the inhibition of the Janus kinase/signal transducer and activation of transcription (JAK/STAT) and mitogen-activated protein kinase (MAPK) pathways. Furthermore, microglia-derived interleukin (IL)-6 and leukaemia inhibitory factor (LIF) were identified as essential molecules for this astrocytic differentiation using neutralizing antibodies and recombinant cytokines. Our results suggest that microglia as well as astrocytes contribute to the integrity of the local environment of NSPCs, and at least IL-6 and LIF released by activated microglia promote astrocytic differentiation of NSPCs via the activation of the JAK/STAT and MAPK pathways.

  5. Peripheral neural activity recording and stimulation system.

    Science.gov (United States)

    Loi, D; Carboni, C; Angius, G; Angotzi, G N; Barbaro, M; Raffo, L; Raspopovic, S; Navarro, X

    2011-08-01

    This paper presents a portable, embedded, microcontroller-based system for bidirectional communication (recording and stimulation) between an electrode, implanted in the peripheral nervous system, and a host computer. The device is able to record and digitize spontaneous and/or evoked neural activities and store them in data files on a PC. In addition, the system has the capability of providing electrical stimulation of peripheral nerves, injecting biphasic current pulses with programmable duration, intensity, and frequency. The recording system provides a highly selective band-pass filter from 800 Hz to 3 kHz, with a gain of 56 dB. The amplification range can be further extended to 96 dB with a variable gain amplifier. The proposed acquisition/stimulation circuitry has been successfully tested through in vivo measurements, implanting a tf-LIFE electrode in the sciatic nerve of a rat. Once implanted, the device showed an input referred noise of 0.83 μVrms, was capable of recording signals below 10 μ V, and generated muscle responses to injected stimuli. The results demonstrate the capability of processing and transmitting neural signals with very low distortion and with a power consumption lower than 1 W. A graphic, user-friendly interface has been developed to facilitate the configuration of the entire system, providing the possibility to activate stimulation and monitor recordings in real time.

  6. Urine macrophage migration inhibitory factor in pediatric systemic lupus erythematosus.

    Science.gov (United States)

    Otukesh, Hasan; Chalian, Majid; Hoseini, Rozita; Chalian, Hamid; Hooman, Nakysa; Bedayat, Arash; Yazdi, Reza Salman; Sabaghi, Saeed; Mahdavi, Saeed

    2007-12-01

    We reported a series of ten patients with lupus nephritis (five patients in the relapse phase and five in the remission phase) and measured the macrophage migration inhibitory factor (MIF), an important pro-inflammatory cytokine with probable role in the pathogenesis of many inflammatory diseases, in their urine samples. MIF/creatinine (Cr) ratio directly correlated with disease activity and it does not have any significant difference between inactive disease and normal ones. We found that the urine MIF/Cr ratio not only differentiates active disease from inactive disease and normal ones but also correlates with the activity indices of renal pathology.

  7. Neurale Netwerken en Radarsystemen (Neural Networks and Radar Systems)

    Science.gov (United States)

    1989-08-01

    34 on "godistribucord geheugon" (zie hoofdstuk 5). Neurologiach on psychologisch ondorzoek dienden bij doze vorm van onderzoek alechta ala...momenteel geen goede algoritmen bekend zijn. Een voorbeeld hiervan is het herkennen van typen objecten aon do hand van gebrekkige, onnauvkeurige of gestoorde...valt onder meer to denken aan do betrouwbaarheld van neurale netwerken. Hot is bekond dat sommige typen netwerken nog steeds redelijk good functioneren

  8. A Spiking Neural Learning Classifier System

    CERN Document Server

    Howard, Gerard; Lanzi, Pier-Luca

    2012-01-01

    Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.

  9. Microbial contamination and inhibitory effect against Streptococcus mutans from fifth-generation bonding systems.

    Science.gov (United States)

    Pinheiro, Sérgio L; Soares, Herbert H; Ribeiro, Mariângela C

    2010-01-01

    The aim of this study was to evaluate microbial contamination and inhibitory effect against Streptococcus mutans (SM) of Prime & Bond (PB), Single Bond (SB) and Excite (EX) bonding systems before use, and after 10 and 20 applications. The bonding material was collected by applying a drop of the material directly on broth brain-heart infusion. The samples were homogenized, diluted and seeded on blood agar plates. To evaluate the inhibitory effect on SM, a drop of each bonding material was dispensed on filter discs and placed on blood agar plates. The Cochran statistical analysis was used to evaluate the total amount of viable bacteria among the different bonding systems. Comparisons between the inhibitory effects on SM were made using the Kruskal-Wallis test. Adhesives SB and EX presented microbial contamination (pcomposition, solvent and application technique of the bonding systems had an influence on contamination by the total number of bacteria and on the inhibitory effect on SM.

  10. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  11. Expert System Based on Data Mining and Neural Networks

    Institute of Scientific and Technical Information of China (English)

    NI Zhi-wei; JIA Rui-yu

    2001-01-01

    On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.

  12. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    Science.gov (United States)

    1990-06-01

    NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

  13. Complexins facilitate neurotransmitter release at excitatory and inhibitory synapses in mammalian central nervous system.

    Science.gov (United States)

    Xue, Mingshan; Stradomska, Alicja; Chen, Hongmei; Brose, Nils; Zhang, Weiqi; Rosenmund, Christian; Reim, Kerstin

    2008-06-03

    Complexins (Cplxs) are key regulators of synaptic exocytosis, but whether they act as facilitators or inhibitors is currently being disputed controversially. We show that genetic deletion of all Cplxs expressed in the mouse brain causes a reduction in Ca(2+)-triggered and spontaneous neurotransmitter release at both excitatory and inhibitory synapses. Our results demonstrate that at mammalian central nervous system synapses, Cplxs facilitate neurotransmitter release and do not simply act as inhibitory clamps of the synaptic vesicle fusion machinery.

  14. Theory of Neural Information Processing Systems

    Energy Technology Data Exchange (ETDEWEB)

    Galla, Tobias [Abdus Salam International Centre for Theoretical Physics and INFM/CNR SISSA-Unit, Strada Costiera 11, I-34014 Trieste (Italy)

    2006-04-07

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 10{sup 11} neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kuehn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard

  15. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  16. Demands on response inhibition processes determine modulations of theta band activity in superior frontal areas and correlations with pupillometry - Implications for the norepinephrine system during inhibitory control.

    Science.gov (United States)

    Dippel, Gabriel; Mückschel, Moritz; Ziemssen, Tjalf; Beste, Christian

    2017-08-15

    Response inhibition processes are important for goal-directed behavior and particularly demanded when it is unlikely to inhibit automatically executed responses. It has been suggested that the norepinephrine (NE) system is important to consider for such likelihood effects. As an indirect measure of the NE system activity we used the pupil diameter and integrated this data with neurophysiological (EEG) data and beamforming analyses. The study shows that inhibitory control processes reflected by theta oscillations are strongly modulated by the likelihood to employ these processes and that these mechanisms were related to neural processes in the SMA and SFG. Probably, the modulations observed for theta band activity may reflect modulations in the encoding of a surprise, or conflict signal. Interestingly, correlation analyses of neuronal activity at the sensor and the source level with pupil diameter data revealed strong correlations that were only seen in the condition where inhibitory control processes were rarely demanded. On the basis of findings and theoretical models suggesting that the pupil diameter can be interpreted as a proximate of NE system activity the results may be interpreted that the NE system modulates inhibitory control processes via theta band activity in the SFB when the likelihood to inhibit a prepotent response tendency is low. From this it may be speculated that the NE system dynamically gains and loses relevance to modulate inhibitory control depending on boundary conditions that determine the mode of responding. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  18. Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

    Institute of Scientific and Technical Information of China (English)

    Gangjun Tan,Tao Song,; Zhihua Chen

    2014-01-01

    Spiking neural P systems with anti-spikes (ASN P sys-tems) are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately an-nihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules (iden-tified by (a, a) and (a, ¯a)) can achieve Turing completeness as number accepting devices.

  19. Two inhibitory systems and CKIs regulate cell cycle exit of mammalian cardiomyocytes after birth

    Energy Technology Data Exchange (ETDEWEB)

    Tane, Shoji; Okayama, Hitomi; Ikenishi, Aiko; Amemiya, Yuki [School of Life Sciences, Faculty of Medicine, Tottori University, Yonago 683-8503 (Japan); Nakayama, Keiichi I. [Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582 (Japan); Takeuchi, Takashi, E-mail: takeuchi@med.tottori-u.ac.jp [School of Life Sciences, Faculty of Medicine, Tottori University, Yonago 683-8503 (Japan)

    2015-10-16

    Mammalian cardiomyocytes actively proliferate during embryonic stages, following which they exit their cell cycle after birth, and the exit is maintained. Previously, we showed that two inhibitory systems (the G1-phase inhibitory system: repression of cyclin D1 expression; the M-phase inhibitory system: inhibition of CDK1 activation) maintain the cell cycle exit of mouse adult cardiomyocytes. We also showed that two CDK inhibitors (CKIs), p21{sup Cip1} and p27{sup Kip1}, regulate the cell cycle exit in a portion of postnatal cardiomyocytes. It remains unknown whether the two inhibitory systems are involved in the cell cycle exit of postnatal cardiomyocytes and whether p21{sup Cip1} and p27{sup Kip1} also inhibit entry to M-phase. Here, we showed that more than 40% of cardiomyocytes entered an additional cell cycle by induction of cyclin D1 expression at postnatal stages, but M-phase entry was inhibited in the majority of cardiomyocytes. Marked cell cycle progression and endoreplication were observed in cardiomyocytes of p21{sup Cip1} knockout mice at 4 weeks of age. In addition, tri- and tetranucleated cardiomyocytes increased significantly in p21{sup Cip1} knockout mice. These data showed that the G1-phase inhibitory system and two CKIs (p21{sup Cip1} and p27{sup Kip1}) inhibit entry to an additional cell cycle in postnatal cardiomyocytes, and that the M-phase inhibitory system and p21{sup Cip1} inhibit M-phase entry of cardiomyocytes which have entered the additional cell cycle. - Highlights: • Many postnatal cardiomyocytes entered an additional cell cycle by cyclin D1 induction. • The majority of cardiomyocytes could not enter M-phase after cyclin D1 induction. • Cell cycle progressed markedly in p21{sup Cip1} knockout mice after postnatal day 14. • Tri- and tetranucleated cardiomyocytes increased in p21{sup Cip1} knockout mice.

  20. Role of neural network models for developing speech systems

    Indian Academy of Sciences (India)

    K Sreenivasa Rao

    2011-10-01

    This paper discusses the application of neural networks for developing different speech systems. Prosodic parameters of speech at syllable level depend on positional, contextual and phonological features of the syllables. In this paper, neural networks are explored to model the prosodic parameters of the syllables from their positional, contextual and phonological features. The prosodic parameters considered in this work are duration and sequence of pitch $(F_0)$ values of the syllables. These prosody models are further examined for applications such as text to speech synthesis, speech recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored for capturing the mapping functions between source and target speakers at source, system and prosodic levels. We have also used neural network models for characterizing the emotions present in speech. For identification of dialects in Hindi, neural network models are used to capture the dialect specific information from spectral and prosodic features of speech.

  1. Neural Systems for Speech and Song in Autism

    Science.gov (United States)

    Lai, Grace; Pantazatos, Spiro P.; Schneider, Harry; Hirsch, Joy

    2012-01-01

    Despite language disabilities in autism, music abilities are frequently preserved. Paradoxically, brain regions associated with these functions typically overlap, enabling investigation of neural organization supporting speech and song in autism. Neural systems sensitive to speech and song were compared in low-functioning autistic and age-matched…

  2. Formalism for the Neural Network of Visual Systems

    NARCIS (Netherlands)

    Stavenga, D.G.; Beersma, D.G.M.

    1975-01-01

    A formalism to describe neural interrelations is developed on the exemplary case of the fly visual system. Absolute and relative indices are employed to identify the position of neural elements within the lattices of the visual ganglia. Illustrative applications as the projection of fly retinula cel

  3. System Identification of X-33 Neural Network

    Science.gov (United States)

    Aggarwal, Shiv

    2003-01-01

    present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

  4. 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.

  5. Identification and estimation algorithm for stochastic neural system.

    Science.gov (United States)

    Nakao, M; Hara, K; Kimura, M; Sato, R

    1984-01-01

    An algorithm for the estimation of stochastic processes in a neural system is presented. This process is defined here as the continuous stochastic process reflecting the dynamics of the neural system which has some inputs and generates output spike trains. The algorithm proposed here is to identify the system parameters and then estimate the stochastic process called neural system process here. These procedures carried out on the basis of the output spike trains which are supposed to be the data observed in the randomly missing way by the threshold time function in the neural system. The algorithm is constructed with the well-known Kalman filters and realizes the estimation of the neural system process by cooperating with the algorithm for the parameter estimation of the threshold time function presented previously (Nakao et al., 1983). The performance of the algorithm is examined by applying it to the various spike trains simulated by some artificial models and also to the neural spike trains recorded in cat's optic tract fibers. The results in these applications are thought to prove the effectiveness of the algorithm proposed here to some extent. Such attempts, we think, will serve to improve the characterizing and modelling techniques of the stochastic neural systems.

  6. Verification and Validation of Neural Networks for Aerospace Systems

    Science.gov (United States)

    Mackall, Dale; Nelson, Stacy; Schumann, Johann

    2002-01-01

    The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.

  7. Biologically inspired neural network controller for an infrared tracking system

    Science.gov (United States)

    Frigo, Janette R.; Tilden, Mark W.

    1999-01-01

    Many biological system exhibit capable, adaptive behavior with a minimal nervous system such as those found in lower invertebrates. Scientists and engineers are studying biological system because these models may have real-world applications. the analog neural controller, herein, is loosely modeled after minimal biological nervous systems. The system consists of the controller and pair of sensor mounted on an actuator. It is implemented with an electrical oscillator network, two IR sensor and a dc motor, used as an actuator for the system. The system tracks an IR target source. The pointing accuracy of this neural network controller is estimated through experimental measurements and a numerical model of the system.

  8. Overstimulation of the inhibitory nervous system plays a role in the pathogenesis of neuromuscular and neurological diseases: a novel hypothesis.

    Science.gov (United States)

    Tuk, Bert

    2016-01-01

    Based upon a thorough review of published clinical observations regarding the inhibitory system, I hypothesize that this system may play a key role in the pathogenesis of a variety of neuromuscular and neurological diseases. Specifically, excitatory overstimulation, which is commonly reported in neuromuscular and neurological diseases, may be a homeostatic response to inhibitory overstimulation. Involvement of the inhibitory system in disease pathogenesis is highly relevant, given that most approaches currently being developed for treating neuromuscular and neurological diseases focus on reducing excitatory activity rather than reducing inhibitory activity.

  9. Interval standard neural network models for nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A neural-network-based robust control design is suggested for control of a class of nonlinear systems. The design approach employs a neural network, whose activation functions satisfy the sector conditions, to approximate the nonlinear system. To improve the approximation performance and to account for the parameter perturbations during operation, a novel neural network model termed standard neural network model (SNNM) is proposed. If the uncertainty is bounded, the SNNM is called an interval SNNM (ISNNM). A state-feedback control law is designed for the nonlinear system modelled by an ISNNM such that the closed-loop system is globally, robustly, and asymptotically stable. The control design equations are shown to be a set of linear matrix inequalities (LMIs) that can be easily solved by available convex optimization algorithms. An example is given to illustrate the control design procedure, and the performance of the proposed approach is compared with that of a related method reported in literature.

  10. Dopamine system: Manager of neural pathways

    Directory of Open Access Journals (Sweden)

    Simon eHong

    2013-12-01

    Full Text Available There are a growing number of roles that midbrain dopamine (DA neurons assume, such as, reward, aversion, alerting and vigor. Here I propose a theory that may be able to explain why the suggested functions of DA came about. It has been suggested that largely parallel cortico-basal ganglia-thalamo-cortico loops exist to control different aspects of behavior. I propose that (1 the midbrain DA system is organized in a similar manner, with different groups of DA neurons corresponding to these parallel neural pathways (NPs. The DA system can be viewed as the manager of these parallel NPs in that it recruits and activates only the task-relevant NPs when they are needed. It is likely that the functions of those NPs that have been consistently activated by the corresponding DA groups are facilitated. I also propose that (2 there are two levels of DA roles: the How and What roles. The How role is encoded in tonic and phasic DA neuron firing patterns and gives a directive to its target NP: how vigorously its function needs to be carried out. The tonic DA firing is to maintain a certain level of DA in the target NPs to support their expected behavioral and mental functions; it is only when a sudden unexpected boost or suppression of activity is required by the relevant target NP that DA neurons in the corresponding NP act in a phasic manner. The What role is the implementational aspect of the role of DA in the target NP, such as binding to D1 receptors to boost working memory. This What aspect of DA explains why DA seems to assume different functions depending on the region of the brain in which it is involved. In terms of the role of the lateral habenula (LHb, the LHb is expected to suppress maladaptive behaviors and mental processes by controlling the DA system. The demand-based smart management by the DA system may have given animals an edge in evolution with adaptive behaviors and a better survival rate in resource-scarce situations.

  11. Vein matching using artificial neural network in vein authentication systems

    Science.gov (United States)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  12. Spiking Neural P Systems with Neuron Division and Budding

    OpenAIRE

    Pan, Linqiang; Paun, Gheorghe; Pérez Jiménez, Mario de Jesús

    2009-01-01

    In order to enhance the e±ciency of spiking neural P systems, we introduce the features of neuron division and neuron budding, which are processes inspired by neural stem cell division. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time. We illustrate this possibility with SAT problem.

  13. Indoor Positioning System Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Hamid Mehmood

    2010-01-01

    Full Text Available Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS has become very useful and popular in recent years. A number of Location Based Services (LBS have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS using Artificial Neural Networks (ANN, which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP, hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method

  14. Implementations of learning control systems using neural networks

    Science.gov (United States)

    Sartori, Michael A.; Antsaklis, Panos J.

    1992-01-01

    The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

  15. Altered Dynamics Between Neural Systems Sub-serving Decisions for Unhealthy Food

    Directory of Open Access Journals (Sweden)

    Qinghua eHe

    2014-11-01

    Full Text Available Using BOLD functional magnetic resonance imaging (fMRI techniques, we examined the relationships between activities in the neural systems elicited by the decision stage of the Iowa Gambling Task (IGT, and food choices of either vegetables or snacks high in fat and sugar. Twenty-three healthy normal weight adolescents and young adults, ranging in age from 14-21, were studied. Neural systems implicated in decision-making and inhibitory control were engaged by having participants perform the IGT during fMRI scanning. The Youth/Adolescent Questionnaire, a food frequency questionnaire, was used to obtain daily food choices. Higher consumption of vegetables correlated with higher activity in prefrontal cortical regions, namely the left superior frontal gyrus (SFG, and lower activity in sub-cortical regions, namely the right insular cortex. In contrast, higher consumption of fatty and sugary snacks correlated with lower activity in the prefrontal regions, combined with higher activity in the sub-cortical, insular cortex.These results provide preliminary support for our hypotheses that unhealthy food choices in real life are reflected by neuronal changes in key neural systems involved in habits, decision-making and self-control processes. These findings have implications for the creation of decision-making based intervention strategies that promote healthier eating.

  16. Altered dynamics between neural systems sub-serving decisions for unhealthy food.

    Science.gov (United States)

    He, Qinghua; Xiao, Lin; Xue, Gui; Wong, Savio; Ames, Susan L; Xie, Bin; Bechara, Antoine

    2014-01-01

    Using BOLD functional magnetic resonance imaging (fMRI) techniques, we examined the relationships between activities in the neural systems elicited by the decision stage of the Iowa Gambling Task (IGT), and food choices of either vegetables or snacks high in fat and sugar. Twenty-three healthy normal weight adolescents and young adults, ranging in age from 14 to 21, were studied. Neural systems implicated in decision-making and inhibitory control were engaged by having participants perform the IGT during fMRI scanning. The Youth/Adolescent Questionnaire, a food frequency questionnaire, was used to obtain daily food choices. Higher consumption of vegetables correlated with higher activity in prefrontal cortical regions, namely the left superior frontal gyrus (SFG), and lower activity in sub-cortical regions, namely the right insular cortex. In contrast, higher consumption of fatty and sugary snacks correlated with lower activity in the prefrontal regions, combined with higher activity in the sub-cortical, insular cortex. These results provide preliminary support for our hypotheses that unhealthy food choices in real life are reflected by neuronal changes in key neural systems involved in habits, decision-making and self-control processes. These findings have implications for the creation of decision-making based intervention strategies that promote healthier eating.

  17. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LI Dong-Mei; WANG Zheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.

  18. Control of Unknown Chaotic Systems Based on Neural Predictive Control

    Institute of Scientific and Technical Information of China (English)

    LIDong-Mei; WANGZheng-Ou

    2003-01-01

    We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.

  19. A survey on RBF Neural Network for Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Henali Sheth

    2014-12-01

    Full Text Available Network security is a hot burning issue nowadays. With the help of technology advancement intruders or hackers are adopting new methods to create different attacks in order to harm network security. Intrusion detection system (IDS is a kind of security software which inspects all incoming and outgoing network traffic and it will generate alerts if any attack or unusual behavior is found in a network. Various approaches are used for IDS such as data mining, neural network, genetic and statistical approach. Among this Neural Network is more suitable approach for IDS. This paper describes RBF neural network approach for Intrusion detection system. RBF is a feed forward and supervise technique of neural network.RBF approach has good classification ability but its performance depends on its parameters. Based on survey we find that RBF approach has some short comings. In order to overcome this we need to do proper optimization of RBF parameters.

  20. Winner-take-all selection in a neural system with delayed feedback

    CERN Document Server

    Brandt, Sebastian F

    2007-01-01

    We consider the effects of temporal delay in a neural feedback system with excitation and inhibition. The topology of our model system reflects the anatomy of the avian isthmic circuitry, a feedback structure found in all classes of vertebrates. We show that the system is capable of performing a `winner-take-all' selection rule for certain combinations of excitatory and inhibitory feedback. In particular, we show that when the time delays are sufficiently large a system with local inhibition and global excitation can function as a `winner-take-all' network and exhibit oscillatory dynamics. We demonstrate how the origin of the oscillations can be attributed to the finite delays through a linear stability analysis.

  1. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  2. Data Process of Diagnose Expert System based on Neural Network

    Directory of Open Access Journals (Sweden)

    Shupeng Zhao

    2013-12-01

    Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network

  3. Nonlinear system identification based on internal recurrent neural networks.

    Science.gov (United States)

    Puscasu, Gheorghe; Codres, Bogdan; Stancu, Alexandru; Murariu, Gabriel

    2009-04-01

    A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.

  4. Representation of neural networks as Lotka-Volterra systems

    Science.gov (United States)

    Moreau, Yves; Louiès, Stéphane; Vandewalle, Joos; Brenig, Léon

    1999-03-01

    We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models—also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoïd. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network.

  5. Data Mining and Neural Network Techniques in Case Based System

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper first puts forward a case-based system framework basedon data mining techniques. Then the paper examines the possibility of using neural n etworks as a method of retrieval in such a case-based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.

  6. Thermal photovoltaic solar integrated system analysis using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering

    2007-07-01

    The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.

  7. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...... a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...

  8. OCT detection of neural activity in American cockroach nervous system

    Science.gov (United States)

    Gorczyńska, Iwona; Wyszkowska, Joanna; Bukowska, Danuta; Ruminski, Daniel; Karnowski, Karol; Stankiewicz, Maria; Wojtkowski, Maciej

    2013-03-01

    We show results of a project which focuses on detection of activity in neural tissue with Optical Coherence Tomography (OCT) methods. Experiments were performed in neural cords dissected from the American cockroach (Periplaneta americana L.). Functional OCT imaging was performed with ultrahigh resolution spectral / Fourier domain OCT system (axial resolution 2.5 μm). Electrical stimulation (voltage pulses) was applied to the sensory cercal nerve of the neural cord. Optical detection of functional activation of the sample was performed in the connective between the terminal abdominal ganglion and the fifth abdominal ganglion. Functional OCT data were collected over time with the OCT beam illuminating selected single point in the connectives (i.e. OCT M-scans were acquired). Phase changes of the OCT signal were analyzed to visualize occurrence of activation in the neural cord. Electrophysiology recordings (microelectrode method) were also performed as a reference method to demonstrate electrical response of the sample to stimulation.

  9. System partitioning on MCM using a new neural network model

    Institute of Scientific and Technical Information of China (English)

    胡卫明; 徐俊华; 严晓浪; 何志钧

    1999-01-01

    A new self-organizing neural network model is presented, which can get rid of some fatal defects facing the Kohonen self-organizing neural network, known as the slow training speed, difficulty in designing neighboring zone, and disability to deal with area constraints directly. Based on the new neural network, a new approach for performance-driven system partitioning on MCM is presented. In the algorithm, the total routing cost between the chips and the circle time are both minimized, while satisfying area and timing constraints. The neural network has a reasonable structure and its training speed is high. The algorithm is able to deal with the large scale circuit partitioning, and has total optimization effect. The algorithm is programmed with Visual C + + language, and experimental result shows that it is an effective method.

  10. Excitatory and inhibitory actions of isoflurane on the cholinergic ascending arousal system of the rat.

    Science.gov (United States)

    Dong, Hai-Long; Fukuda, Satoru; Murata, Eri; Higuchi, Takashi

    2006-01-01

    The cholinergic arousal systems are known to critically regulate the state of consciousness. The aim of this study was to determine the effect of isoflurane on the inhibitory or excitatory neurotransmitters efflux in important nuclei within the cholinergic arousal system using in vivo intracerebral microdialysis. The efflux of glutamate, gamma-aminobutyric acid (GABA), or acetylcholine in the posterior hypothalamus (PH), the basal forebrain (BF), and the somatosensory cortex (S1BF) of rats was detected using intracerebral microdialysis under an awake condition and at 0.5-2.0 minimum alveolar concentration (MAC) isoflurane anesthesia. The intrabasalis perfusion of alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) and N-methyl-D-aspartate on the cortical acetylcholine effluxes was also examined under both conditions. Isoflurane had no influence on the glutamate and GABA efflux in the PH, whereas in the BF, it dose-dependently increased glutamate efflux and decreased GABA efflux. A transient increase in glutamate efflux at 1.0 MAC and a decrease in GABA at 0.5-1.5 MAC were observed in the S1BF. Isoflurane dose-dependently decreased acetylcholine efflux in the S1BF. Perfusion of the BF with AMPA increased acetylcholine efflux in the S1BF with electroencephalographic activation during 0.75 MAC isoflurane anesthesia, suggesting an inhibitory action of isoflurane on AMPA receptors in the BF. However, N-methyl-D-aspartate had no effect on these parameters. Isoflurane induces both excitatory and inhibitory actions in the cholinergic arousal system. The predominant inhibitory action of isoflurane over its excitatory action at the BF would result in the decrease in the acetylcholine efflux in the S1BF.

  11. FPGA implementation of a pyramidal Weightless Neural Networks learning system.

    Science.gov (United States)

    Al-Alawi, Raida

    2003-08-01

    A hardware architecture of a Probabilistic Logic Neuron (PLN) is presented. The suggested model facilitates the on-chip learning of pyramidal Weightless Neural Networks using a modified probabilistic search reward/penalty training algorithm. The penalization strategy of the training algorithm depends on a predefined parameter called the probabilistic search interval. A complete Weightless Neural Network (WNN) learning system is modeled and implemented on Xilinx XC4005E Field Programmable Gate Array (FPGA), allowing its architecture to be configurable. Various experiments have been conducted to examine the feasibility and performance of the WNN learning system. Results show that the system has a fast convergence rate and good generalization ability.

  12. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  13. NNSYSID - toolbox for system identification with neural networks

    DEFF Research Database (Denmark)

    Norgaard, M.; Ravn, Ole; Poulsen, Niels Kjølstad

    2002-01-01

    The NNSYSID toolset for System Identification has been developed as an add on to MATLAB(R). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains a number of nonlinear model structures based on neural networks, effective training algorithms...

  14. Neural Network Predictive Control Based Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Ali Mohamed Yousef

    2012-04-01

    Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.

  15. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop...... a suitable learning algorithm -- a continuous-time version of a temporal differential Hebbian learning algorithm for pulsed neural systems with non-linear synapses -- as well as circuits for the electronic implementation. Measurements from an experimental CMOS chip are presented. Finally, we use our test...

  16. FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM

    Institute of Scientific and Technical Information of China (English)

    Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang

    2003-01-01

    The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.

  17. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

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

  18. Reliability Modeling of Microelectromechanical Systems Using Neural Networks

    Science.gov (United States)

    Perera. J. Sebastian

    2000-01-01

    Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

  19. Frontolimbic neural circuit changes in emotional processing and inhibitory control associated with clinical improvement following transference-focused psychotherapy in borderline personality disorder.

    Science.gov (United States)

    Perez, David L; Vago, David R; Pan, Hong; Root, James; Tuescher, Oliver; Fuchs, Benjamin H; Leung, Lorene; Epstein, Jane; Cain, Nicole M; Clarkin, John F; Lenzenweger, Mark F; Kernberg, Otto F; Levy, Kenneth N; Silbersweig, David A; Stern, Emily

    2016-01-01

    Borderline personality disorder (BPD) is characterized by self-regulation deficits, including impulsivity and affective lability. Transference-focused psychotherapy (TFP) is an evidence-based treatment proven to reduce symptoms across multiple cognitive-emotional domains in BPD. This pilot study aimed to investigate neural activation associated with, and predictive of, clinical improvement in emotional and behavioral regulation in BPD following TFP. BPD subjects (n = 10) were scanned pre- and post-TFP treatment using a within-subjects design. A disorder-specific emotional-linguistic go/no-go functional magnetic resonance imaging paradigm was used to probe the interaction between negative emotional processing and inhibitory control. Analyses demonstrated significant treatment-related effects with relative increased dorsal prefrontal (dorsal anterior cingulate, dorsolateral prefrontal, and frontopolar cortices) activation, and relative decreased ventrolateral prefrontal cortex and hippocampal activation following treatment. Clinical improvement in constraint correlated positively with relative increased left dorsal anterior cingulate cortex activation. Clinical improvement in affective lability correlated positively with left posterior-medial orbitofrontal cortex/ventral striatum activation, and negatively with right amygdala/parahippocampal activation. Post-treatment improvements in constraint were predicted by pre-treatment right dorsal anterior cingulate cortex hypoactivation, and pre-treatment left posterior-medial orbitofrontal cortex/ventral striatum hypoactivation predicted improvements in affective lability. These preliminary findings demonstrate potential TFP-associated alterations in frontolimbic circuitry and begin to identify neural mechanisms associated with a psychodynamically oriented psychotherapy. © 2015 The Authors. Psychiatry and Clinical Neurosciences © 2015 Japanese Society of Psychiatry and Neurology.

  20. Identification and estimation algorithm for stochastic neural system. II.

    Science.gov (United States)

    Nakao, M; Hara, K; Kimura, M; Sato, R

    1985-01-01

    The algorithm for identifying the stochastic neural system and estimating the system process which reflects the dynamics of the neural network are presented in this paper. The analogous algorithm has been proposed in our preceding paper (Nakao et al., 1984), which was based on the randomly missed observations of a system process only. Since the previous algorithm mentioned above was subject to an unfavorable effect of consecutively missed observations, to reduce such an effect the algorithm proposed here is designed additionally to observe an intensity process in a neural spike train as the information for the estimation. The algorithm is constructed with the extended Kalman filters because it is naturally expected that a nonlinear and time variant structure is necessary for the filters to realize the observation of an intensity process by means of mapping from a system process to an intensity process. The performance of the algorithm is examined by applying it to some artificial neural systems and also to cat's visual nervous systems. The results in these applications are thought to prove the effectiveness of the algorithm proposed here and its superiority to the algorithm proposed previously.

  1. Approximation Problems in System Identification With Neural Networks

    Institute of Scientific and Technical Information of China (English)

    陈天平

    1994-01-01

    In this paper, the capability of neural networks and some approximation problens in system identification with neural networks are investigated. Some results are given: (i) For any function g ∈Llocp (R1) ∩S’ (R1) to be an Lp-Tauber-Wiener function, it is necessary and sufficient that g is not apolynomial; (ii) If g∈(Lp TW), then the set of is dense in Lp(K)’ (iii) It is proved that bycompositions of some functions of one variable, one can approximate continuous functional defined on compact Lp(K) and continuous operators from compact Lp1(K1) to LP2(K2). These results confirm the capability of neural networks in identifying dynamic systems.

  2. Application of dynamic recurrent neural networks in nonlinear system identification

    Science.gov (United States)

    Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang

    2006-11-01

    An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

  3. Cortical neurodynamics of inhibitory control.

    Science.gov (United States)

    Hwang, Kai; Ghuman, Avniel S; Manoach, Dara S; Jones, Stephanie R; Luna, Beatriz

    2014-07-16

    The ability to inhibit prepotent responses is critical for successful goal-directed behaviors. To investigate the neural basis of inhibitory control, we conducted a magnetoencephalography study where human participants performed the antisaccade task. Results indicated that neural oscillations in the prefrontal cortex (PFC) showed significant task modulations in preparation to suppress saccades. Before successfully inhibiting a saccade, beta-band power (18-38 Hz) in the lateral PFC and alpha-band power (10-18 Hz) in the frontal eye field (FEF) increased. Trial-by-trial prestimulus FEF alpha-band power predicted successful saccadic inhibition. Further, inhibitory control enhanced cross-frequency amplitude coupling between PFC beta-band (18-38 Hz) activity and FEF alpha-band activity, and the coupling appeared to be initiated by the PFC. Our results suggest a generalized mechanism for top-down inhibitory control: prefrontal beta-band activity initiates alpha-band activity for functional inhibition of the effector and/or sensory system.

  4. Expert,Neural and Fuzzy Systems in Process Planning

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    Computer aided process planning (CAPP) aims at improving efficiency, quali t y, and productivity in a manufacturing concern through reducing lead-times and costs by utilizing better manufacturing practices thus improving competitiveness in the market. CAPP attempts to capture the thoughts and methods of the experie nced process planner. Variant systems are understandable, generative systems can plan new parts. Expert systems increase flexibility, fuzzy logic captures vague knowledge while neural networks learn. The combination of fuzzy, neural and exp ert system technologies is necessary to capture and utilize the process planning logic. A system that maintains the dependability and clarity of variant systems , is capable of planning new parts, and improves itself through learning is neede d by industry.

  5. Engineering neural systems for high-level problem solving.

    Science.gov (United States)

    Sylvester, Jared; Reggia, James

    2016-07-01

    There is a long-standing, sometimes contentious debate in AI concerning the relative merits of a symbolic, top-down approach vs. a neural, bottom-up approach to engineering intelligent machine behaviors. While neurocomputational methods excel at lower-level cognitive tasks (incremental learning for pattern classification, low-level sensorimotor control, fault tolerance and processing of noisy data, etc.), they are largely non-competitive with top-down symbolic methods for tasks involving high-level cognitive problem solving (goal-directed reasoning, metacognition, planning, etc.). Here we take a step towards addressing this limitation by developing a purely neural framework named galis. Our goal in this work is to integrate top-down (non-symbolic) control of a neural network system with more traditional bottom-up neural computations. galis is based on attractor networks that can be "programmed" with temporal sequences of hand-crafted instructions that control problem solving by gating the activity retention of, communication between, and learning done by other neural networks. We demonstrate the effectiveness of this approach by showing that it can be applied successfully to solve sequential card matching problems, using both human performance and a top-down symbolic algorithm as experimental controls. Solving this kind of problem makes use of top-down attention control and the binding together of visual features in ways that are easy for symbolic AI systems but not for neural networks to achieve. Our model can not only be instructed on how to solve card matching problems successfully, but its performance also qualitatively (and sometimes quantitatively) matches the performance of both human subjects that we had perform the same task and the top-down symbolic algorithm that we used as an experimental control. We conclude that the core principles underlying the galis framework provide a promising approach to engineering purely neurocomputational systems for problem

  6. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  7. Human leukemia inhibitory factor produced by the ExpressTec method from rice (Oryza sativa L.) is active in human neural stem cells and mouse induced pluripotent stem cells

    Science.gov (United States)

    Alfano, Randall; Youngblood, Bradford A; Zhang, Deshui; Huang, Ning; MacDonald, Clinton C

    2014-01-01

    Stem cell-based therapy has the potential to treat an array of human diseases. However, to study the therapeutic potential and safety of these cells, a scalable cell culture medium is needed that is free of human or bovine-derived serum proteins. Thus, cost-effective recombinant serum proteins and cytokines are needed to produce such mediums. One such cytokine, leukemia inhibitory factor (LIF), has been shown to be a critical paracrine factor that maintains stem cell pluripotency in murine embryonic stem cells and human naïve stem cells while simultaneously inhibiting differentiation. We recently produced recombinant human LIF (rhLIF) in a rice-based protein expression system known as ExpressTec.12 We described expression of rice-derived rhLIF and demonstrated its biological equivalency to E. coli-derived rhLIF in traditional and embryonic mouse stem cell systems. Here we describe the expression yield of rice-derived rhLIF and the scale up production capacity. We provide further evidence of the efficacy of rice-derived rhLIF in additional stem cell systems including human neural stem cells and mouse induced pluripotent stem (iPS) cells. The expression level, biological activity, and potential for production at commercial scale of rice-derived rhLIF provides a proof-of-principal for ExpressTec-derived proteins to produce regulatory-friendly, high performance, and dependable stem cell media. PMID:24776984

  8. A rule-based neural controller for inverted pendulum system.

    Science.gov (United States)

    Hao, J; Vandewalle, J; Tan, S

    1993-03-01

    This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.

  9. Macrophage migration inhibitory factor in cerebrospinal fluid from patients with central nervous system infection

    DEFF Research Database (Denmark)

    Ostergaard, Christian; Benfield, Thomas

    2009-01-01

    ABSTRACT: INTRODUCTION: Macrophage Migration Inhibitory Factor (MIF) plays an essential pathophysiological role in septic shock; however, its role in central nervous system infection (CNS) remains to be defined. METHODS: The aim of the present study was to investigate cerebrospinal fluid (CSF...... suspected of but had no evidence of CNS infection. RESULTS: CSF MIF levels were significantly higher in patients with purulent meningitis of known aetiology (8639 ng/L (3344-20600)) as compared to patients with purulent meningitis of unknown aetiology (2209 ng/L (1516-6550), Mann Whitney test, P=0...

  10. Neural transduction in Xenopus laevis lateral line system.

    Science.gov (United States)

    Strelioff, D; Honrubia, V

    1978-03-01

    1. The process of neural excitation in hair cell systems was studied in an in vitro preparation of the Xenopus laevis (African clawed toad) lateral line organ. A specially designed stimulus chamber was used to apply accurately controlled pressure, water movement, or electrical stimuli, and to record the neural responses of the two afferent fibers innervating each organ or stitch. The objective of the study was to determine the characteristics of the neural responses to these stimuli, and thus gain insight into the transduction process. 2. A sustained deflection of the hair cell cilia due to a constant flow of water past the capula resulted in a maintained change in the mean firing rate (MFR) of the afferent fibers. The data also demonstrated that the neural response was proportional to the velocity of the water flow and indicated that both deflection and movement of the cilia were the effective physiological stimuli for this hair cell system. 3. The preparations responded to sinusoidal water movements (past the capula) over the entire frequency range of the stimulus chamber, 0.1-130 Hz, and were most sensitive between 10 and 40 Hz. The variation of the MFR and the percent modulation indicated that the average dynamic range of each organ was 23.5 dB. 4. The thresholds, if any, for sustained pressure changes and for sinusoidal pressure variations in the absence of water movements were very high. Due to the limitations of the stimulus chamber it was not possible to generate pressure stimuli of sufficient magnitude to elicit a neural response without also generating suprathreshold water-movement stimuli. Sustained pressures had no detectable effect on the neural response to water-movement stimuli. 5. The preparations were very sensitive to electrical potentials applied across the toad skin on which the hair cells were located. Potentials which made the ciliated surfaces of the hair cells positive with respect to their bases increased the MFR of the fibers, whereas

  11. Neural mechanisms of selective attention in the somatosensory system.

    Science.gov (United States)

    Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

    2016-09-01

    Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates.

  12. Frequency-difference-dependent stochastic resonance in neural systems

    Science.gov (United States)

    Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

    2017-08-01

    Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

  13. Robust nonlinear system identification using neural-network models.

    Science.gov (United States)

    Lu, S; Basar, T

    1998-01-01

    We study the problem of identification for nonlinear systems in the presence of unknown driving noise, using both feedforward multilayer neural network and radial basis function network models. Our objective is to resolve the difficulty associated with the persistency of excitation condition inherent to the standard schemes in the neural identification literature. This difficulty is circumvented here by a novel formulation and by using a new class of identification algorithms recently obtained by Didinsky et al. We show how these algorithms can be exploited to successfully identify the nonlinearity in the system using neural-network models. By embedding the original problem in one with noise-perturbed state measurements, we present a class of identifiers (under L1 and L2 cost criteria) which secure a good approximant for the system nonlinearity provided that some global optimization technique is used. In this respect, many available learning algorithms in the current neural-network literature, e.g., the backpropagation scheme and the genetic algorithms-based scheme, with slight modifications, can ensure the identification of the system nonlinearity. Subsequently, we address the same problem under a third, worst case L(infinity) criterion for an RBF modeling. We present a neural-network version of an H(infinity)-based identification algorithm from Didinsky et al and show how, along with an appropriate choice of control input to enhance excitation, under both full-state-derivative information (FSDI) and noise-perturbed full-state-information (NPFSI), it leads to satisfaction of a relevant persistency of excitation condition, and thereby to robust identification of the nonlinearity. Results from several simulation studies have been included to demonstrate the effectiveness of these algorithms.

  14. Neural Network Control of a Magnetically Suspended Rotor System

    Science.gov (United States)

    Choi, Benjamin B.

    1998-01-01

    Magnetic bearings offer significant advantages because they do not come into contact with other parts during operation, which can reduce maintenance. Higher speeds, no friction, no lubrication, weight reduction, precise position control, and active damping make them far superior to conventional contact bearings. However, there are technical barriers that limit the application of this technology in industry. One of them is the need for a nonlinear controller that can overcome the system nonlinearity and uncertainty inherent in magnetic bearings. At the NASA Lewis Research Center, a neural network was selected as a nonlinear controller because it generates a neural model without any detailed information regarding the internal working of the magnetic bearing system. It can be used even for systems that are too complex for an accurate system model to be derived. A feed-forward architecture with a back-propagation learning algorithm was selected because of its proven performance, accuracy, and relatively easy implementation.

  15. A Miniaturized System for Neural Signal Acquiring and Processing

    Institute of Scientific and Technical Information of China (English)

    WANG Min; GAO Guang-hong; XIANG Dong-sheng; CAO Mao-yong; JIA Ai-bin; DING Lei; KONG Hui-min

    2008-01-01

    To collect neural activity data from awake, behaving freely animals, we develop miniaturized implantable recording system by the modern chip:Programmable System on Chip(PSoC) and through chronic electrodes in the cortex. With PSoC family member CY8C29466,the system completed operational and instrument amplifiers, filters, timers, AD convertors, and serial communication, etc. The signal processing was dealt with virtual instrument technology. All of these factors can significantly affect the price and development cycle of the project. The result showed that the system was able to record and analyze neural extrocellular discharge generated by neurons continuously for a week or more. This is very useful for the interdisciplinary research of neuroscience and information engineering technique.The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.

  16. 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.

  17. Synthesis of recurrent neural networks for dynamical system simulation.

    Science.gov (United States)

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time.

  18. Variable Neural Adaptive Robust Control: A Switched System Approach

    Energy Technology Data Exchange (ETDEWEB)

    Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

    2015-05-01

    Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.

  19. Adaptive Neural Network Controller for Thermogenerator Angular Velocity Stabilization System

    OpenAIRE

    2013-01-01

    The paper presents an analytical and simulation approach for the selection of activation functions for the class of neural network controllers for ship’s thermogenerator angular velocity stabilization system. Such systems can be found in many ships. A Lyapunov-like stability analysis is performed in order to obtain a weight update law. A number of simulations were performed to find the best activation function using integral error criteria and statistical T-tests.

  20. Peripheral Nervous System Genes Expressed in Central Neurons Induce Growth on Inhibitory Substrates

    Science.gov (United States)

    Buchser, William J.; Smith, Robin P.; Pardinas, Jose R.; Haddox, Candace L.; Hutson, Thomas; Moon, Lawrence; Hoffman, Stanley R.; Bixby, John L.; Lemmon, Vance P.

    2012-01-01

    Trauma to the spinal cord and brain can result in irreparable loss of function. This failure of recovery is in part due to inhibition of axon regeneration by myelin and chondroitin sulfate proteoglycans (CSPGs). Peripheral nervous system (PNS) neurons exhibit increased regenerative ability compared to central nervous system neurons, even in the presence of inhibitory environments. Previously, we identified over a thousand genes differentially expressed in PNS neurons relative to CNS neurons. These genes represent intrinsic differences that may account for the PNS’s enhanced regenerative ability. Cerebellar neurons were transfected with cDNAs for each of these PNS genes to assess their ability to enhance neurite growth on inhibitory (CSPG) or permissive (laminin) substrates. Using high content analysis, we evaluated the phenotypic profile of each neuron to extract meaningful data for over 1100 genes. Several known growth associated proteins potentiated neurite growth on laminin. Most interestingly, novel genes were identified that promoted neurite growth on CSPGs (GPX3, EIF2B5, RBMX). Bioinformatic approaches also uncovered a number of novel gene families that altered neurite growth of CNS neurons. PMID:22701605

  1. Peripheral nervous system genes expressed in central neurons induce growth on inhibitory substrates.

    Directory of Open Access Journals (Sweden)

    William J Buchser

    Full Text Available Trauma to the spinal cord and brain can result in irreparable loss of function. This failure of recovery is in part due to inhibition of axon regeneration by myelin and chondroitin sulfate proteoglycans (CSPGs. Peripheral nervous system (PNS neurons exhibit increased regenerative ability compared to central nervous system neurons, even in the presence of inhibitory environments. Previously, we identified over a thousand genes differentially expressed in PNS neurons relative to CNS neurons. These genes represent intrinsic differences that may account for the PNS's enhanced regenerative ability. Cerebellar neurons were transfected with cDNAs for each of these PNS genes to assess their ability to enhance neurite growth on inhibitory (CSPG or permissive (laminin substrates. Using high content analysis, we evaluated the phenotypic profile of each neuron to extract meaningful data for over 1100 genes. Several known growth associated proteins potentiated neurite growth on laminin. Most interestingly, novel genes were identified that promoted neurite growth on CSPGs (GPX3, EIF2B5, RBMX. Bioinformatic approaches also uncovered a number of novel gene families that altered neurite growth of CNS neurons.

  2. Peripheral nervous system genes expressed in central neurons induce growth on inhibitory substrates.

    Science.gov (United States)

    Buchser, William J; Smith, Robin P; Pardinas, Jose R; Haddox, Candace L; Hutson, Thomas; Moon, Lawrence; Hoffman, Stanley R; Bixby, John L; Lemmon, Vance P

    2012-01-01

    Trauma to the spinal cord and brain can result in irreparable loss of function. This failure of recovery is in part due to inhibition of axon regeneration by myelin and chondroitin sulfate proteoglycans (CSPGs). Peripheral nervous system (PNS) neurons exhibit increased regenerative ability compared to central nervous system neurons, even in the presence of inhibitory environments. Previously, we identified over a thousand genes differentially expressed in PNS neurons relative to CNS neurons. These genes represent intrinsic differences that may account for the PNS's enhanced regenerative ability. Cerebellar neurons were transfected with cDNAs for each of these PNS genes to assess their ability to enhance neurite growth on inhibitory (CSPG) or permissive (laminin) substrates. Using high content analysis, we evaluated the phenotypic profile of each neuron to extract meaningful data for over 1100 genes. Several known growth associated proteins potentiated neurite growth on laminin. Most interestingly, novel genes were identified that promoted neurite growth on CSPGs (GPX3, EIF2B5, RBMX). Bioinformatic approaches also uncovered a number of novel gene families that altered neurite growth of CNS neurons.

  3. ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

    Institute of Scientific and Technical Information of China (English)

    X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

    2003-01-01

    An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

  4. Superparamagnetic segmentation by excitable neural systems.

    Science.gov (United States)

    Neirotti, Juan P; Kurcbart, Samuel M; Caticha, Nestor

    2003-09-01

    Magnetic modeling for clustering or segmentation purposes can either associate the image data to external quenched fields or to the interactions among a set of auxiliary variables. The latter gives rise to superparamagnetic segmentation and is usually done with Potts systems. We have used the superparamagnetic clustering technique to segment images, with the aid of different associated systems. Results using Potts model are comparable to those obtained using excitable FitzHugh-Nagumo and Morris-Lecar model neurons. Interactions between the associated system components are a function of the difference of luminosity on a gray scale of neighbor pixels and the difference of membrane potential.

  5. Perception Neural Networks for Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    Wang Xiaoli

    2012-11-01

    Full Text Available In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC. An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively.

  6. Parameter estimation in space systems using recurrent neural networks

    Science.gov (United States)

    Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

    1991-01-01

    The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

  7. The neural correlates of priming emotion and reward systems for conflict processing in alcoholics.

    Science.gov (United States)

    Schulte, T; Jung, Y-C; Sullivan, E V; Pfefferbaum, A; Serventi, M; Müller-Oehring, E M

    2016-11-04

    Emotional dysregulation in alcoholism (ALC) may result from disturbed inhibitory mechanisms. We therefore tested emotion and alcohol cue reactivity and inhibitory processes using negative priming. To test the neural correlates of cue reactivity and negative priming, 26 ALC and 26 age-matched controls underwent functional MRI performing a Stroop color match-to-sample task. In cue reactivity trials, task-irrelevant emotion and alcohol-related pictures were interspersed between color samples and color words. In negative priming trials, pictures primed the semantic content of an alcohol or emotion Stroop word. Behaviorally, both groups showed response facilitation to picture cue trials and response inhibition to primed trials. For cue reactivity to emotion and alcohol pictures, ALC showed midbrain-limbic activation. By contrast, controls activated frontoparietal executive control regions. Greater midbrain-hippocampal activation in ALC correlated with higher amounts of lifetime alcohol consumption and higher anxiety. With negative priming, ALC exhibited frontal cortical but not midbrain-hippocampal activation, similar to the pattern observed in controls. Higher frontal activation to alcohol-priming correlated with less craving and to emotion-priming with fewer depressive symptoms. The findings suggest that neurofunctional systems in ALC can be primed to deal with upcoming emotion- and alcohol-related conflict and can overcome the prepotent midbrain-limbic cue reactivity response.

  8. Serotonin-immunoreactive neural system and contractile system in the hydroid Cladonema (Cnidaria, Hydrozoa).

    Science.gov (United States)

    Mayorova, T D; Kosevich, I A

    2013-12-01

    Serotonin is a widespread neurotransmitter which is present in almost all animal phyla including lower metazoans such as Cnidaria. Serotonin detected in the polyps of several cnidarian species participates in the functioning of a neural system. It was suggested that serotonin coordinates polyp behavior. For example, serotonin may be involved in muscle contraction and/or cnidocyte discharge. However, the role of serotonin in cnidarians is not revealed completely yet. The aim of this study was to investigate the neural system of Cladonema radiatum polyps. We detected the net of serotonin-positive processes within the whole hydranth body using anti-serotonin antibodies. The hypostome and tentacles had denser neural net in comparison with the gastric region. Electron microscopy revealed muscle processes throughout the hydranth body. Neural processes with specific vesicles and neurotubules in their cytoplasm were also shown at an ultrastructural level. This work demonstrates the structure of serotonin-positive neural system and smooth muscle layer in C. radiatum hydranths.

  9. Analysis of the DWPF glass pouring system using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Calloway, T.B. Jr.; Jantzen, C.M. [Westinghouse Savannah River Co., Aiken, SC (United States). Savannah River Technology Center; Medich, L.; Spennato, N. [Pavillion Technologies, Inc., Austin, TX (United States)

    1997-08-05

    Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of {+-} 0.35 inwc (< 2% of the instrument`s measured range, R{sup 2} = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R{sub 2} = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers.

  10. Inverse synchronizations in coupled time-delay systems with inhibitory coupling.

    Science.gov (United States)

    Senthilkumar, D V; Kurths, J; Lakshmanan, M

    2009-06-01

    Transitions between inverse anticipatory, inverse complete, and inverse lag synchronizations are shown to occur as a function of the coupling delay in unidirectionally coupled time-delay systems with inhibitory coupling. We have also shown that the same general asymptotic stability condition obtained using the Krasovskii-Lyapunov functional theory can be valid for the cases where (i) both the coefficients of the Delta(t) (error variable) and Delta(tau)=Delta(t-tau) (error variable with delay) terms in the error equation corresponding to the synchronization manifold are time independent and (ii) the coefficient of the Delta term is time independent, while that of the Delta(tau) term is time dependent. The existence of different kinds of synchronization is corroborated using similarity function, probability of synchronization, and also from changes in the spectrum of Lyapunov exponents of the coupled time-delay systems.

  11. Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

    Directory of Open Access Journals (Sweden)

    David Breuer

    2014-03-01

    Full Text Available How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

  12. NNIC—neural network image compressor for satellite positioning system

    Science.gov (United States)

    Danchenko, Pavel; Lifshits, Feodor; Orion, Itzhak; Koren, Sion; Solomon, Alan D.; Mark, Shlomo

    2007-04-01

    We have developed an algorithm, based on novel techniques of data compression and neural networks for the optimal positioning of a satellite. The algorithm is described in detail, and examples of its application are given. The heart of this algorithm is the program NNIC—neural network image compressor. This program was developed for compression color and grayscale images with artificial neural networks (ANNs). NNIC applies three different methods for compression. Two of them are based on neural networks architectures—multilayer perceptron and kohonen network. The third is based on a widely used method of discrete cosine transform, the basis for the JPEG standard. The program also serves as a tool for determining numerical and visual quality parameters of compression and comparison between different methods. A number of advantages and disadvantages of the compression using ANNs were discovered in the course of the present research, some of them presented in this report. The thrust of the report is the discussion of ANNs implementation problems for modern platforms, such as a satellite positioning system that include intensive image flowing and processing.

  13. Neural-Fuzzy Approach for System Identification.

    NARCIS (Netherlands)

    Tien, B.T.

    1997-01-01

    Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such cases a nonli

  14. Sensitive periods for the functional specialization of the neural system for human face processing.

    Science.gov (United States)

    Röder, Brigitte; Ley, Pia; Shenoy, Bhamy H; Kekunnaya, Ramesh; Bottari, Davide

    2013-10-15

    The aim of the study was to identify possible sensitive phases in the development of the processing system for human faces. We tested the neural processing of faces in 11 humans who had been blind from birth and had undergone cataract surgery between 2 mo and 14 y of age. Pictures of faces and houses, scrambled versions of these pictures, and pictures of butterflies were presented while event-related potentials were recorded. Participants had to respond to the pictures of butterflies (targets) only. All participants, even those who had been blind from birth for several years, were able to categorize the pictures and to detect the targets. In healthy controls and in a group of visually impaired individuals with a history of developmental or incomplete congenital cataracts, the well-known enhancement of the N170 (negative peak around 170 ms) event-related potential to faces emerged, but a face-sensitive response was not observed in humans with a history of congenital dense cataracts. By contrast, this group showed a similar N170 response to all visual stimuli, which was indistinguishable from the N170 response to faces in the controls. The face-sensitive N170 response has been associated with the structural encoding of faces. Therefore, these data provide evidence for the hypothesis that the functional differentiation of category-specific neural representations in humans, presumably involving the elaboration of inhibitory circuits, is dependent on experience and linked to a sensitive period. Such functional specialization of neural systems seems necessary to archive high processing proficiency.

  15. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  16. Exploring the function of neural oscillations in early sensory systems

    Directory of Open Access Journals (Sweden)

    Kilian Koepsell

    2010-05-01

    Full Text Available Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether or not neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems as we ask how neural oscillations arise and how they might encode information about the stimulus. We will highlight recent work in the early visual pathway that shows how oscillations can multiplex different types of signals to increase the amount of information that spike trains encode and transmit. Last, we will describe oscillation-based models of visual processing and explore how they might guide further research.

  17. Using Neural Networks to improve classical Operating System Fingerprinting techniques

    CERN Document Server

    Sarraute, Carlos

    2010-01-01

    We present remote Operating System detection as an inference problem: given a set of observations (the target host responses to a set of tests), we want to infer the OS type which most probably generated these observations. Classical techniques used to perform this analysis present several limitations. To improve the analysis, we have developed tools using neural networks and Statistics tools. We present two working modules: one which uses DCE-RPC endpoints to distinguish Windows versions, and another which uses Nmap signatures to distinguish different version of Windows, Linux, Solaris, OpenBSD, FreeBSD and NetBSD systems. We explain the details of the topology and inner workings of the neural networks used, and the fine tuning of their parameters. Finally we show positive experimental results.

  18. Spiking Neural P Systems with Neuron Division and Dissolution

    Science.gov (United States)

    Liu, Xiyu; Wang, Wenping

    2016-01-01

    Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a “time-space trade-off” strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al. PMID:27627104

  19. Neuroeconomics--from neural systems to economic behaviour.

    Science.gov (United States)

    Braeutigam, Sven

    2005-11-15

    Neuroeconomics is a new and highly interdisciplinary field. Drawing from theories and methodologies employed in both economics and neuroscience, it aims at understanding the neural systems supporting and affecting economically relevant behaviour in real-life situations. Although incomplete, the evidence is beginning to clarify with the possibility that neuroeconomic methodology might eventually trace whole processes of economically relevant behaviour. This paper accompanies the author's ConNEcs 2004 keynote speech on applications of neuroeconomic research.

  20. Adaptive control of system with hysteresis using neural networks

    Institute of Scientific and Technical Information of China (English)

    Li Chuntao; Tan Yonghong

    2006-01-01

    An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network (NN), whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.

  1. Neural systems supporting the control of affective and cognitive conflicts.

    Science.gov (United States)

    Ochsner, Kevin N; Hughes, Brent; Robertson, Elaine R; Cooper, Jeffrey C; Gabrieli, John D E

    2009-09-01

    Although many studies have examined the neural bases of controlling cognitive responses, the neural systems for controlling conflicts between competing affective responses remain unclear. To address the neural correlates of affective conflict and their relationship to cognitive conflict, the present study collected whole-brain fMRI data during two versions of the Eriksen flanker task. For these tasks, participants indicated either the valence (affective task) or the semantic category (cognitive task) of a central target word while ignoring flanking words that mapped onto either the same (congruent) or a different (incongruent) response as the target. Overall, contrasts of incongruent > congruent trials showed that bilateral dorsal ACC, posterior medial frontal cortex, and dorsolateral pFC were active during both kinds of conflict, whereas rostral medial pFC and left ventrolateral pFC were differentially active during affective or cognitive conflict, respectively. Individual difference analyses showed that separate regions of rostral cingulate/ventromedial pFC and left ventrolateral pFC were positively correlated with the magnitude of response time interference. Taken together, the findings that controlling affective and cognitive conflicts depends upon both common and distinct systems have important implications for understanding the organization of control systems in general and their potential dysfunction in clinical disorders.

  2. 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.

  3. Role of leukemia inhibitory factor in the nervous system and its pathology.

    Science.gov (United States)

    Ostasov, Pavel; Houdek, Zbynek; Cendelin, Jan; Kralickova, Milena

    2015-01-01

    Leukemia inhibitory factor (LIF) is a multi-function cytokine that has various effects on different tissues and cell types in rodents and humans; however, its insufficiency has a relatively mild impact. This could explain why only some aspects of LIF activity are in the time-light, whereas other aspects are not well known. In this review, the LIF structure, signaling pathway, and primary roles in the development and function of an organism are reviewed, and the effects of LIF on stem cell growth and differentiation, which are important for its use in cell culturing, are described. The focus is on the roles of LIF in central nervous system development and on the modulation of its physiological functions as well as the involvement of LIF in the pathogenesis of brain diseases and injuries. Finally, LIF and its signaling pathway are discussed as potential targets of therapeutic interventions to influence both negative phenomena and regenerative processes following brain injury.

  4. Effects of Subminimum Inhibitory Concentrations of Antibiotics on the Pasteurella multocida Proteome: A Systems Approach

    Directory of Open Access Journals (Sweden)

    Bindu Nanduri

    2008-01-01

    Full Text Available To identify key regulators of subminimum inhibitory concentration (sub-MIC antibiotic response in the Pasteurella multocida proteome, we applied systems approaches. Using 2D-LC-ESI-MS2, we achieved 53% proteome coverage. To study the differential protein expression in response to sub-MIC antibiotics in the context of protein interaction networks, we inferred P. multocida Pm70 protein interaction network from orthologous proteins. We then overlaid the differential protein expression data onto the P. multocida protein interaction network to study the bacterial response. We identified proteins that could enhance antimicrobial activity. Overall compensatory response to antibiotics was characterized by altered expression of proteins involved in purine metabolism, stress response, and cell envelope permeability.

  5. Fuzzy-Neural Automatic Daylight Control System

    Directory of Open Access Journals (Sweden)

    Grif H. Şt.

    2011-12-01

    Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.

  6. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  7. Motivation alters impression formation and related neural systems.

    Science.gov (United States)

    Hughes, Brent L; Zaki, Jamil; Ambady, Nalini

    2017-01-01

    Observers frequently form impressions of other people based on complex or conflicting information. Rather than being objective, these impressions are often biased by observers' motives. For instance, observers often downplay negative information they learn about ingroup members. Here, we characterize the neural systems associated with biased impression formation. Participants learned positive and negative information about ingroup and outgroup social targets. Following this information, participants worsened their impressions of outgroup, but not ingroup, targets. This tendency was associated with a failure to engage neural structures including lateral prefrontal cortex, dorsal anterior cingulate cortex, temporoparietal junction, Insula and Precuneus when processing negative information about ingroup (but not outgroup) targets. To the extent that participants engaged these regions while learning negative information about ingroup members, they exhibited less ingroup bias in their impressions. These data are consistent with a model of 'effortless bias', under which perceivers fail to process goal-inconsistent information in order to maintain desired conclusions.

  8. Adult neural stem cells in the mammalian central nervous system

    Institute of Scientific and Technical Information of China (English)

    Dengke K Ma; Michael A Bonaguidi; Guo-li Ming; Hongjun Song

    2009-01-01

    Neural stem cells (NSCs) are present not only during the embryonic development but also in the adult brain of all mammalian species, including humans. Stem cell niche architecture in vivo enables adult NSCs to continuously generate functional neurons in specific brain regions throughout life. The adult neurogenesis process is subject to dynamic regulation by various physiological, pathological and pharmacological stimuli. Multipotent adult NSCs also appear to be intrinsically plastic, amenable to genetic programing during normal differentiation, and to epigenetic reprograming during de-differentiation into pluripotency. Increasing evidence suggests that adult NSCs significantly contribute to specialized neural functions under physiological and pathological conditions. Fully understanding the biology of adult NSCs will provide crucial insights into both the etiology and potential therapeutic interventions of major brain disorders. Here, we review recent progress on adult NSCs of the mammalian central nervous system, in-cluding topics on their identity, niche, function, plasticity, and emerging roles in cancer and regenerative medicine.

  9. VLSI neural system architecture for finite ring recursive reduction.

    Science.gov (United States)

    Zhang, D; Jullien, G A

    1996-12-01

    The use of neural-like networks to implement finite ring computations has been presented in a previous paper. This paper develops efficient VLSI neural system architecture for the finite ring recursive reduction (FRRR), including module reduction, MSB carry iteration and feedforward processing. These techniques deal with the basic principles involved in constructing a FRRR, and their implementations are efficiently matched to the VLSI medium. Compared with the other structure models for finite ring computation (e.g. modification of binary arithmetic logic and bit-steered ROM's), the FRRR structure has the lowest area complexity in silicon while maintaining a high throughput rate. Examples of several implementations are used to illustrate the effectiveness of the FRRR architecture.

  10. Intelligent systems II complete approximation by neural network operators

    CERN Document Server

    Anastassiou, George A

    2016-01-01

    This monograph is the continuation and completion of the monograph, “Intelligent Systems: Approximation by Artificial Neural Networks” written by the same author and published 2011 by Springer. The book you hold in hand presents the complete recent and original work of the author in approximation by neural networks. Chapters are written in a self-contained style and can be read independently. Advanced courses and seminars can be taught out of this brief book. All necessary background and motivations are given per chapter. A related list of references is given also per chapter. The book’s results are expected to find applications in many areas of applied mathematics, computer science and engineering. As such this monograph is suitable for researchers, graduate students, and seminars of the above subjects, also for all science and engineering libraries.  .

  11. Interplay between Network Topology and Dynamics in Neural Systems

    CERN Document Server

    Johnson, Samuel

    2013-01-01

    This thesis is a compendium of research which brings together ideas from the fields of Complex Networks and Computational Neuroscience to address two questions regarding neural systems: 1) How the activity of neurons, via synaptic changes, can shape the topology of the network they form part of, and 2) How the resulting network structure, in its turn, might condition aspects of brain behaviour. Although the emphasis is on neural networks, several theoretical findings which are relevant for complex networks in general are presented -- such as a method for studying network evolution as a stochastic process, or a theory that allows for ensembles of correlated networks, and sets of dynamical elements thereon, to be treated mathematically and computationally in a model-independent manner. Some of the results are used to explain experimental data -- certain properties of brain tissue, the spontaneous emergence of correlations in all kinds of networks... -- and predictions regarding statistical aspects of the centra...

  12. Simulating Spiking Neural P systems without delays using GPUs

    CERN Document Server

    Cabarle, Francis; Martinez-del-Amor, Miguel A

    2011-01-01

    We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel natu...

  13. The inhibitory role of sympathetic nervous system in the Ca2+-dependent proteolysis of skeletal muscle

    Directory of Open Access Journals (Sweden)

    L.C.C. Navegantes

    2009-01-01

    Full Text Available Mammalian cells contain several proteolytic systems to carry out the degradative processes and complex regulatory mechanisms to prevent excessive protein breakdown. Among these systems, the Ca2+-activated proteolytic system involves the cysteine proteases denoted calpains, and their inhibitor, calpastatin. Despite the rapid progress in molecular research on calpains and calpastatin, the physiological role and regulatory mechanisms of these proteins remain obscure. Interest in the adrenergic effect on Ca2+-dependent proteolysis has been stimulated by the finding that the administration of β2-agonists induces muscle hypertrophy and prevents the loss of muscle mass in a variety of pathologic conditions in which calpains are activated. This review summarizes evidence indicating that the sympathetic nervous system produces anabolic, protein-sparing effects on skeletal muscle protein metabolism. Studies are reviewed, which indicate that epinephrine secreted by the adrenal medulla and norepinephrine released from adrenergic terminals have inhibitory effects on Ca2+-dependent protein degradation, mainly in oxidative muscles, by increasing calpastatin levels. Evidence is also presented that this antiproteolytic effect, which occurs under both basal conditions and in stress situations, seems to be mediated by β2- and β3-adrenoceptors and cAMP-dependent pathways. The understanding of the precise mechanisms by which catecholamines promote muscle anabolic effects may have therapeutic value for the treatment of muscle-wasting conditions and may enhance muscle growth in farm species for economic and nutritional purposes.

  14. Neural networks and logical reasoning systems: a translation table.

    Science.gov (United States)

    Martins, J; Mendes, R V

    2001-04-01

    A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics.

  15. 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...

  16. Synchronization of an uncertain chaotic system via recurrent neural networks

    Institute of Scientific and Technical Information of China (English)

    谭文; 王耀南

    2005-01-01

    Incorporating distributed recurrent networks with high-order connections between neurons, the identification and synchronization problem of an unknown chaotic system in the presence of unmodelled dynamics is investigated. Based on the Lyapunov stability theory, the weights learning algorithm for the recurrent high-order neural network model is presented. Also, analytical results concerning the stability properties of the scheme are obtained. Then adaptive control law for eliminating synchronization error of uncertain chaotic plant is developed via Lyapunov methodology.The proposed scheme is applied to model and synchronize an unknown Rossler system.

  17. Adaptive neural-based fuzzy modeling for biological systems.

    Science.gov (United States)

    Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong

    2013-04-01

    The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.

  18. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  19. Properties of the stimulus router system, a novel neural prosthesis.

    Science.gov (United States)

    Gan, Liu Shi; Prochazka, Arthur

    2010-02-01

    Various types of neural prostheses (NPs) have been developed to restore motor function after neural injury. Surface NPs are noninvasive and inexpensive, but are often poorly selective, activating nontargeted muscles and cutaneous sensory nerves that can cause discomfort or pain. Implantable NPs are highly selective, but invasive and costly. The stimulus router system (SRS) is a novel NP consisting of fully implanted leads that "capture" and route some of the current flowing between a pair of surface electrodes to the vicinity of a target nerve. An SRS lead consists of a "pick-up" terminal that is implanted subcutaneously under one of the surface electrodes and a "delivery" terminal that is secured on or near the target nerve. We have published a preliminary report on the basic properties of the SRS [L. S. Gan , "A new means of transcutaneous coupling for neural prostheses," IEEE Trans. Biomed. Eng., vol. 54, no. 3, pp. 509-517, Mar. 2007]. Here, we further characterize the SRS and identify aspects that maximize its performance as a motor NP. The surface current needed to activate nerves with an SRS, was found to depend on the proximity of the delivery terminal(s) to the nerve, electrode configurations, contact areas of the surface electrodes and implanted terminals, and the distance between the surface anode and the delivery terminal.

  20. Neural Network Based Popularity Prediction For IPTV System

    Directory of Open Access Journals (Sweden)

    Jun Li

    2012-12-01

    Full Text Available Internet protocol television (IPTV, being an emerging Internet application, plays an important and indispensable role in our daily life. In order to maximize user experience and on the same time to minimize service cost, we must take into pay attention to how to reduce the storage and transport costs. A lot of previous work has been done before to do this. There is a challenging problem in this: how to predict the popularities of videos as accurate as possible. To solve the problem, this paper presents a Neural Network model for the popularity prediction of the programs in the IPTV system. And we use the actual historical logs to validate our method. The historical logs are divided to two parts, one is used to train the neural network by extract input/output vectors, and the other part is used to verify the model. The experimental results from our validation show the Neural Network based method can gain better accuracy than the comparative method.

  1. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  2. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.

  3. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  4. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  5. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  6. Olfactory systems and neural circuits that modulate predator odor fear.

    Science.gov (United States)

    Takahashi, Lorey K

    2014-01-01

    When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator

  7. Hardware implementation of a neural vision system based on a neural network using integrated and fire neurons

    Science.gov (United States)

    González, M.; Lamela, H.; Jiménez, M.; Gimeno, J.; Ruiz-Llata, M.

    2007-04-01

    In this paper we present the scheme for a control circuit used in an image processing system which is to be implemented in a neural network which has a high level of connectivity and reconfiguration of neurons for integration and trigger based on the Address-Event Representation. This scheme will be employed as a pre-processing stage for a vision system which employs as its core processing an Optical Broadcast Neural Network (OBNN). [Optical Engineering letters 42 (9), 2488(2003)]. The proposed vision system allows the possibility to introduce patterns from any acquisition system of images, for posterior processing.

  8. Fuzzy neural network technique for system state forecasting.

    Science.gov (United States)

    Li, Dezhi; Wang, Wilson; Ismail, Fathy

    2013-10-01

    In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

  9. Inhibitory role of the serotonergic system on estrogen receptor α expression in the female rat hypothalamus.

    Science.gov (United States)

    Ito, Hiroyuki; Shimogawa, Yuji; Kohagura, Daisuke; Moriizumi, Tetsuji; Yamanouchi, Korehito

    2014-11-07

    The role of the serotonergic system in regulating the expression of estrogen receptor (ER) α in the hypothalamus was investigated in ovariectomized rats by injecting a serotonin synthesis inhibitor, parachlorophenylalanine (PCPA), or by destroying the dorsal raphe nucleus (DR). The number of ERα-immunoreactive (ir) cells was counted in the anteroventral periventricular nucleus in the preoptic area (AVPV), ventrolateral ventromedial hypothalamic nucleus (vlVMN), and arcuate nucleus (ARCN). Seven days after ovariectomy, 100mg/kg PCPA or saline was injected daily for 4 days. Alternatively, radiofrequency lesioning of the DR (DRL) or sham lesions were made on the same time of ovariectomy. One-day after the last injection of PCPA or 7 days after brain surgery, the brain was fixed for immunostaining of ERα and the number of ERα-ir cell were counted in the nuclei of interest. The mean number of ERα-ir cells/mm(3) (density) in the AVPV of the PCPA or DRL groups was statistically higher than that in the saline or sham group. In the vlVMN and ARCN of the PCPA or DRL groups, the mean density of ERα-ir cells was comparable to the saline or sham groups. These results suggest that the serotonergic system of the DR plays an inhibitory role on the expression of ERα in the AVPV, but not in the vlVMN and ARCN. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. Comparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System

    Directory of Open Access Journals (Sweden)

    Sheela Tiwari

    2013-08-01

    Full Text Available This paperexplores theapplicationof artificial neural networksfor online identification of a multimachinepower system.Arecurrent neural networkhas been proposedas the identifier of the two area, four machinesystemwhich is a benchmark system for studying electromechanical oscillations in multimachine powersystems. This neural identifier is trained using the static Backpropagation algorithm. The emphasis of thepaper is on investigating the performance of the variants of the Backpropagation algorithm in training theneural identifier. The paper also compares the performances of the neural identifiers trained usingvariantsof the Backpropagation algorithmover a wide range of operating conditions.The simulation resultsestablish a satisfactory performance of the trained neural identifiers in identification of the test powersystem

  11. Fault Tolerant Neural Network for ECG Signal Classification Systems

    Directory of Open Access Journals (Sweden)

    MERAH, M.

    2011-08-01

    Full Text Available The aim of this paper is to apply a new robust hardware Artificial Neural Network (ANN for ECG classification systems. This ANN includes a penalization criterion which makes the performances in terms of robustness. Specifically, in this method, the ANN weights are normalized using the auto-prune method. Simulations performed on the MIT ? BIH ECG signals, have shown that significant robustness improvements are obtained regarding potential hardware artificial neuron failures. Moreover, we show that the proposed design achieves better generalization performances, compared to the standard back-propagation algorithm.

  12. Structural Health Monitoring Using Neural Network Based Vibrational System Identification

    CERN Document Server

    Sofge, Donald A

    2007-01-01

    Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integrity for composite structures.

  13. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  14. Dual inductive link coil design for a neural recording system.

    Science.gov (United States)

    Rush, Alexander; Troyk, Philip R

    2011-01-01

    This paper reports an approach to the physical design of the coils used in a dual inductive link to provide two-way wireless communication and power for a neural recording system. The design approach makes use of an analytic model of the link performance in terms of the physical parameters of the link, which allows physical parameters to be iterated on a computer rather than on the bench to find the optimal design within the physical restrictions imposed. In particular, this approach was used to choose the optimal implant data coil sizing to maximize the difference between the contributions of the constructive and destructive paths of the reverse telemetry signal.

  15. Application of Adaptive Neural Network Observer in Chaotic Systems

    Directory of Open Access Journals (Sweden)

    Milad Malekzadeh

    2014-01-01

    Full Text Available Chaos control is an important subject in control theory. Chaos control usually confronts with some problems due to unavailability of states or losing the system characteristics during the modeling process. In this situation, using an appropriate observer in control strategy may overcome the problem. In this paper, states are estimated using an observer without having complete prior information from nonlinear term based on neural network. Simulation results verify performance of the proposed structure in estimating nonlinear term specifically for an online practical use.

  16. Overstimulation of the inhibitory nervous system plays a role in the pathogenesis of neuromuscular and neurological diseases: a novel hypothesis [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Bert Tuk

    2016-08-01

    Full Text Available Based upon a thorough review of published clinical observations regarding the inhibitory system, I hypothesize that this system may play a key role in the pathogenesis of a variety of neuromuscular and neurological diseases. Specifically, excitatory overstimulation, which is commonly reported in neuromuscular and neurological diseases, may be a homeostatic response to inhibitory overstimulation. Involvement of the inhibitory system in disease pathogenesis is highly relevant, given that most approaches currently being developed for treating neuromuscular and neurological diseases focus on reducing excitatory activity rather than reducing inhibitory activity.

  17. Neural systems supporting and affecting economically relevant behavior

    Directory of Open Access Journals (Sweden)

    Braeutigam S

    2012-05-01

    Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

  18. Oscillatory Dynamics and Oscillation Death in Complex Networks Consisting of Both Excitatory and Inhibitory Nodes

    Institute of Scientific and Technical Information of China (English)

    张立升; 廖旭红; 弭元元; 谷伟风; 胡岗

    2012-01-01

    Zn neural networks, both excitatory and inhibitory cells play important roles in determining the functions of systems. Various dynamical networks have been proposed as artificial neural networks to study the properties of biological systems where the influences of excitatory nodes have been extensively investigated while those of inhibitory nodes have been studied much less. In this paper, we consider a model of oscillatory networks of excitable Boolean maps consisting of both excitatory and inhibitory nodes, focusing on the roles of inhibitory nodes. We find that inhibitory nodes in sparse networks (smM1 average connection degree) play decisive roles in weakening oscillations, and oscillation death occurs after continual weakening of oscillation for sufficiently high inhibitory node density. In the sharp contrast, increasing inhibitory nodes in dense networks may result in the increase of oscillation amplitude and sudden oscillation death at much lower inhibitory node density and the nearly highest excitation activities. Mechanism under these peculiar behaviors of dense networks is explained by the competition of the duplex effects of inhibitory nodes.

  19. Exposure to sub-inhibitory concentrations of cefotaxime enhances the systemic colonization of Salmonella Typhimurium in BALB/c mice

    Science.gov (United States)

    Molina-Quiroz, Roberto C.; Silva, Cecilia A.; Molina, Cristian F.; Leiva, Lorenzo E.; Reyes-Cerpa, Sebastián; Contreras, Inés; Santiviago, Carlos A.

    2015-01-01

    It has been proposed that sub-inhibitory concentrations of antibiotics play a role in virulence modulation. In this study, we evaluated the ability of Salmonella enterica serovar Typhimurium (hereafter S. Typhimurium) to colonize systemically BALB/c mice after exposure to a sub-inhibitory concentration of cefotaxime (CTX). In vivo competition assays showed a fivefold increase in systemic colonization of CTX-exposed bacteria when compared to untreated bacteria. To identify the molecular mechanisms involved in this phenomenon, we carried out a high-throughput genetic screen. A transposon library of S. Typhimurium mutants was subjected to negative selection in the presence of a sub-inhibitory concentration of CTX and genes related to anaerobic metabolism, biosynthesis of purines, pyrimidines, amino acids and other metabolites were identified as needed to survive in this condition. In addition, an impaired ability for oxygen consumption was observed when bacteria were cultured in the presence of a sub-inhibitory concentration of CTX. Altogether, our data indicate that exposure to sub-lethal concentrations of CTX increases the systemic colonization of S. Typhimurium in BALB/c mice in part by the establishment of a fitness alteration conducive to anaerobic metabolism. PMID:26468132

  20. Exposure to sub-inhibitory concentrations of cefotaxime enhances the systemic colonization of Salmonella Typhimurium in BALB/c mice.

    Science.gov (United States)

    Molina-Quiroz, Roberto C; Silva, Cecilia A; Molina, Cristian F; Leiva, Lorenzo E; Reyes-Cerpa, Sebastián; Contreras, Inés; Santiviago, Carlos A

    2015-10-01

    It has been proposed that sub-inhibitory concentrations of antibiotics play a role in virulence modulation. In this study, we evaluated the ability of Salmonella enterica serovar Typhimurium (hereafter S. Typhimurium) to colonize systemically BALB/c mice after exposure to a sub-inhibitory concentration of cefotaxime (CTX). In vivo competition assays showed a fivefold increase in systemic colonization of CTX-exposed bacteria when compared to untreated bacteria. To identify the molecular mechanisms involved in this phenomenon, we carried out a high-throughput genetic screen. A transposon library of S. Typhimurium mutants was subjected to negative selection in the presence of a sub-inhibitory concentration of CTX and genes related to anaerobic metabolism, biosynthesis of purines, pyrimidines, amino acids and other metabolites were identified as needed to survive in this condition. In addition, an impaired ability for oxygen consumption was observed when bacteria were cultured in the presence of a sub-inhibitory concentration of CTX. Altogether, our data indicate that exposure to sub-lethal concentrations of CTX increases the systemic colonization of S. Typhimurium in BALB/c mice in part by the establishment of a fitness alteration conducive to anaerobic metabolism.

  1. Recurrent Neural Network for Single Machine Power System Stabilizer

    Directory of Open Access Journals (Sweden)

    Widi Aribowo

    2010-04-01

    Full Text Available In this paper, recurrent neural network (RNN is used to design power system stabilizer (PSS due to its advantage on the dependence not only on present input but also on past condition. A RNN-PSS is able to capture the dynamic response of a system without any delays caused by external feedback, primarily by the internal feedback loop in recurrent neuron. In this paper, RNNPSS consists of a RNN-identifier and a RNN-controller. The RNN-Identifier functions as the tracker of dynamics characteristics of the plant, while the RNN-controller is used to damp the system’s low frequency oscillations. Simulation results using MATLAB demonstrate that the RNNPSS can successfully damp out oscillation and improve the performance of the system.

  2. Incomplete fuzzy data processing systems using artificial neural network

    Science.gov (United States)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  3. Artificial neural network analysis of triple effect absorption refrigeration systems

    Energy Technology Data Exchange (ETDEWEB)

    Hajizadeh Aghdam, A. [Department of Mechanical Engineering, Islamic Azad University (Iran, Islamic Republic of)], email: a.hajizadeh@iaukashan.ac.ir; Nazmara, H.; Farzaneh, B. [Department of Mechanical Engineering, University of Tabriz (Iran, Islamic Republic of)], email: h.nazmara@nioec.org, email: b_farzaneh_ms@yahoo.com

    2011-07-01

    In this study, artificial neural networks are utilized to predict the performance of triple effect series and parallel flow absorption refrigeration systems, with lithium bromide/water as the working fluid. Important parameters such as high generator and evaporator temperatures were varied and their effects on the performance characteristics of the refrigeration unit were observed. Absorption refrigeration systems make energy savings possible because they can use heat energy to produce cooling, in place of the electricity used for conventional vapour compression chillers. In addition, non-conventional sources of energy (such as solar, waste heat, and geothermal) can be utilized as their primary energy input. Moreover, absorption units use environmentally friendly working fluid pairs instead of CFCs and HCFCs, which affect the ozone layer. Triple effect absorption cycles were analysed. Results apply for both series and parallel flow systems. A relative preference for parallel-flow over series-flow is also shown.

  4. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  5. Neural Network Enhanced Structure Determination of Osteoporosis, Immune System, and Radiation Repair Proteins Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed innovation will utilize self learning neural network technology to determine the structure of osteoporosis, immune system disease, and excess radiation...

  6. An Aircraft Navigation System Fault Diagnosis Method Based on Optimized Neural Network Algorithm

    Institute of Scientific and Technical Information of China (English)

    Jean-dedieu Weyepe

    2014-01-01

    Air data and inertial reference system (ADIRS) is one of the complex sub-system in the aircraft navigation system and it plays an important role into the flight safety of the aircraft. This paper propose an optimize neural network algorithm which is a combination of neural network and ant colony algorithm to improve efficiency of maintenance engineer job task.

  7. Neural networks and dynamical system techniques for volcanic tremor analysis

    Directory of Open Access Journals (Sweden)

    R. Carniel

    1996-06-01

    Full Text Available A volcano can be seen as a dynamical system, the number of state variables being its dimension N. The state is usually confined on a manifold with a lower dimension f, manifold which is characteristic of a persistent «structural configuration». A change in this manifold may be a hint that something is happening to the dynamics of the volcano, possibly leading to a paroxysmal phase. In this work the original state space of the volcano dynamical system is substituted by a pseudo state space reconstructed by the method of time-delayed coordinates, with suitably chosen lag time and embedding dimension, from experimental time series of seismic activity, i.e. volcanic tremor recorded at Stromboli volcano. The monitoring is done by a neural network which first learns the dynamics of the persistent tremor and then tries to detect structural changes in its behaviour.

  8. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  9. Living ordered neural networks as model systems for signal processing

    Science.gov (United States)

    Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.

    2007-06-01

    Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.

  10. Fuzzy stochastic neural network model for structural system identification

    Science.gov (United States)

    Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong

    2017-01-01

    This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.

  11. Effects of Fast Simple Numerical Calculation Training on Neural Systems.

    Science.gov (United States)

    Takeuchi, Hikaru; Nagase, Tomomi; Taki, Yasuyuki; Sassa, Yuko; Hashizume, Hiroshi; Nouchi, Rui; Kawashima, Ryuta

    2016-01-01

    Cognitive training, including fast simple numerical calculation (FSNC), has been shown to improve performance on untrained processing speed and executive function tasks in the elderly. However, the effects of FSNC training on cognitive functions in the young and on neural mechanisms remain unknown. We investigated the effects of 1-week intensive FSNC training on cognitive function, regional gray matter volume (rGMV), and regional cerebral blood flow at rest (resting rCBF) in healthy young adults. FSNC training was associated with improvements in performance on simple processing speed, speeded executive functioning, and simple and complex arithmetic tasks. FSNC training was associated with a reduction in rGMV and an increase in resting rCBF in the frontopolar areas and a weak but widespread increase in resting rCBF in an anatomical cluster in the posterior region. These results provide direct evidence that FSNC training alone can improve performance on processing speed and executive function tasks as well as plasticity of brain structures and perfusion. Our results also indicate that changes in neural systems in the frontopolar areas may underlie these cognitive improvements.

  12. Adaptive Neural Control Design For a Class of Nonlinear Time-delay Systems

    Institute of Scientific and Technical Information of China (English)

    FENG Ling-ling; ZHANG Wei

    2014-01-01

    This paper proposes an indirect adaptive neural control scheme for a class of nonlinear systems with time delays. Based on the backstepping technique and Lyapunov–Krasovskii functional method are combined to construct the indirect adaptive neural controller. The proposed indirect adaptive neural controller guarantees that the state variables converge to a small neighborhood of the origin and all the signals of the closed-loop system are bounded. Finally, an example is used to show the effectiveness of the proposed control strategy.

  13. BOOK REVIEW: Theory of Neural Information Processing Systems

    Science.gov (United States)

    Galla, Tobias

    2006-04-01

    It is difficult not to be amazed by the ability of the human brain to process, to structure and to memorize information. Even by the toughest standards the behaviour of this network of about 1011 neurons qualifies as complex, and both the scientific community and the public take great interest in the growing field of neuroscience. The scientific endeavour to learn more about the function of the brain as an information processing system is here a truly interdisciplinary one, with important contributions from biology, computer science, physics, engineering and mathematics as the authors quite rightly point out in the introduction of their book. The role of the theoretical disciplines here is to provide mathematical models of information processing systems and the tools to study them. These models and tools are at the centre of the material covered in the book by Coolen, Kühn and Sollich. The book is divided into five parts, providing basic introductory material on neural network models as well as the details of advanced techniques to study them. A mathematical appendix complements the main text. The range of topics is extremely broad, still the presentation is concise and the book well arranged. To stress the breadth of the book let me just mention a few keywords here: the material ranges from the basics of perceptrons and recurrent network architectures to more advanced aspects such as Bayesian learning and support vector machines; Shannon's theory of information and the definition of entropy are discussed, and a chapter on Amari's information geometry is not missing either. Finally the statistical mechanics chapters cover Gardner theory and the replica analysis of the Hopfield model, not without being preceded by a brief introduction of the basic concepts of equilibrium statistical physics. The book also contains a part on effective theories of the macroscopic dynamics of neural networks. Many dynamical aspects of neural networks are usually hard to find in the

  14. Neural mechanism of facilitation system during physical fatigue.

    Directory of Open Access Journals (Sweden)

    Masaaki Tanaka

    Full Text Available An enhanced facilitation system caused by motivational input plays an important role in supporting performance during physical fatigue. We tried to clarify the neural mechanisms of the facilitation system during physical fatigue using magnetoencephalography (MEG and a classical conditioning technique. Twelve right-handed volunteers participated in this study. Participants underwent MEG recording during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. Thereafter, fatigue-inducing maximum handgrip trials were performed for 10 min; the metronome sounds were started 5 min after the beginning of the handgrip trials. The metronome sounds were used as conditioned stimuli and maximum handgrip trials as unconditioned stimuli. The next day, they were randomly assigned to two groups in a single-blinded, two-crossover fashion to undergo two types of MEG recordings, that is, for the control and motivation sessions, during the imagery of maximum grips of the right hand guided by metronome sounds for 10 min. The alpha-band event-related desynchronizations (ERDs of the motivation session relative to the control session within the time windows of 500 to 700 and 800 to 900 ms after the onset of handgrip cue sounds were identified in the sensorimotor areas. In addition, the alpha-band ERD within the time window of 400 to 500 ms was identified in the right dorsolateral prefrontal cortex (Brodmann's area 46. The ERD level in the right dorsolateral prefrontal cortex was positively associated with that in the sensorimotor areas within the time window of 500 to 700 ms. These results suggest that the right dorsolateral prefrontal cortex is involved in the neural substrates of the facilitation system and activates the sensorimotor areas during physical fatigue.

  15. Optimal Workflow Scheduling in Critical Infrastructure Systems with Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Vukmirović

    2012-04-01

    Full Text Available Critical infrastructure systems (CISs, such as power grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated. This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

  16. Cortical Neurodynamics of Inhibitory Control

    OpenAIRE

    Hwang, Kai; Ghuman, Avniel S.; Dara S Manoach; Stephanie R. Jones; Luna, Beatriz

    2014-01-01

    The ability to inhibit prepotent responses is critical for successful goal-directed behaviors. To investigate the neural basis of inhibitory control, we conducted a magnetoencephalography study where human participants performed the antisaccade task. Results indicated that neural oscillations in the prefrontal cortex (PFC) showed significant task modulations in preparation to suppress saccades. Before successfully inhibiting a saccade, beta-band power (18–38 Hz) in the lateral PFC and alpha-b...

  17. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases.

  18. Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm

    Science.gov (United States)

    Xu, Liming

    The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

  19. Recurrent neural networks-based multivariable system PID predictive control

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi

    2007-01-01

    A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.

  20. Remote Neural Pendants In A Welding-Control System

    Science.gov (United States)

    Venable, Richard A.; Bucher, Joseph H.

    1995-01-01

    Neural network integrated circuits enhance functionalities of both remote terminals (called "pendants") and communication links, without necessitating installation of additional wires in links. Makes possible to incorporate many features into pendant, including real-time display of critical welding parameters and other process information, capability for communication between technician at pendant and host computer or technician elsewhere in system, and switches and potentiometers through which technician at pendant exerts remote control over such critical aspects of welding process as current, voltage, rate of travel, flow of gas, starting, and stopping. Other potential manufacturing applications include control of spray coating and of curing of composite materials. Potential nonmanufacturing uses include remote control of heating, air conditioning, and lighting in electrically noisy and otherwise hostile environments.

  1. Chemosensory signals and their receptors in the olfactory neural system.

    Science.gov (United States)

    Ihara, S; Yoshikawa, K; Touhara, K

    2013-12-19

    Chemical communication is widely used among various organisms to obtain essential information from their environment required for life. Although a large variety of molecules have been shown to act as chemical cues, the molecular and neural basis underlying the behaviors elicited by these molecules has been revealed for only a limited number of molecules. Here, we review the current knowledge regarding the signaling molecules whose flow from receptor to specific behavior has been characterized. Discussing the molecules utilized by mice, insects, and the worm, we focus on how each organism has optimized its reception system to suit its living style. We also highlight how the production of these signaling molecules is regulated, an area in which considerable progress has been recently made.

  2. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  3. Stochastic Neural Field Theory and the System-Size Expansion

    KAUST Repository

    Bressloff, Paul C.

    2010-01-01

    We analyze a master equation formulation of stochastic neurodynamics for a network of synaptically coupled homogeneous neuronal populations each consisting of N identical neurons. The state of the network is specified by the fraction of active or spiking neurons in each population, and transition rates are chosen so that in the thermodynamic or deterministic limit (N → ∞) we recover standard activity-based or voltage-based rate models. We derive the lowest order corrections to these rate equations for large but finite N using two different approximation schemes, one based on the Van Kampen system-size expansion and the other based on path integral methods. Both methods yield the same series expansion of the moment equations, which at O(1/N) can be truncated to form a closed system of equations for the first-and second-order moments. Taking a continuum limit of the moment equations while keeping the system size N fixed generates a system of integrodifferential equations for the mean and covariance of the corresponding stochastic neural field model. We also show how the path integral approach can be used to study large deviation or rare event statistics underlying escape from the basin of attraction of a stable fixed point of the mean-field dynamics; such an analysis is not possible using the system-size expansion since the latter cannot accurately determine exponentially small transitions. © by SIAM.

  4. Output-back fuzzy logic systems and equivalence with feedback neural networks

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.

  5. Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems

    NARCIS (Netherlands)

    Vuurpijl, L.G.

    1998-01-01

    In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to

  6. Platforms for artificial neural networks : neurosimulators and performance prediction of MIMD-parallel systems

    NARCIS (Netherlands)

    Vuurpijl, L.G.

    1998-01-01

    In this thesis, two platforms for simulating artificial neural networks are discussed: MIMD-parallel processor systems as an execution platform and neurosimulators as a research and development platform. Because of the parallelism encountered in neural networks, distributed processor systems seem to

  7. Ligation of Signal Inhibitory Receptor on Leukocytes-1 Suppresses the Release of Neutrophil Extracellular Traps in Systemic Lupus Erythematosus

    OpenAIRE

    Kristof Van Avondt; Ruth Fritsch-Stork; Derksen, Ronald H W M; Linde Meyaard

    2013-01-01

    Neutrophil extracellular traps (NETs) have been implicated in the pathogenesis of systemic Lupus erythematosus (SLE), since netting neutrophils release potentially immunogenic autoantigens including histones, LL37, human neutrophil peptide (HNP), and self-DNA. In turn, these NETs activate plasmacytoid dendritic cells resulting in aggravation of inflammation and disease. How suppression of NET formation can be targeted for treatment has not been reported yet. Signal Inhibitory Receptor on Leuk...

  8. An integrated architecture of adaptive neural network control for dynamic systems

    Energy Technology Data Exchange (ETDEWEB)

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

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

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

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

  10. An Information Theoretic Model of Information Processing in the Drosophila Olfactory System: the Role of Inhibitory Neurons for System Efficiency

    Directory of Open Access Journals (Sweden)

    Faramarz eFaghihi

    2013-12-01

    Full Text Available Fruit flies (Drosophila melanogaster rely on their olfactory system to process environmental information. This information has to be transmitted without system-relevant loss by the olfactory system to deeper brain areas for learning. Here we study the role of several parameters of the fly's olfactory system and the environment and how they influence olfactory information transmission. We have designed an abstract model of the antennal lobe, the mushroom body and the inhibitory circuitry. Mutual information between the olfactory environment, simulated in terms of different odor concentrations, and a sub-population of intrinsic mushroom body neurons (Kenyon cells was calculated to quantify the efficiency of information transmission. With this method we study, on the one hand, the effect of different connectivity rates between olfactory projection neurons and firing thresholds of Kenyon cells. On the other hand, we analyze the influence of inhibition on mutual information between environment and mushroom body. Our simulations show an expected linear relation between the connectivity rate between the antennal lobe and the mushroom body and firing threshold of the Kenyon cells to obtain maximum mutual information for both low and high odor concentrations. However, contradicting all-day experiences, high odor concentrations cause a drastic, and unrealistic, decrease in mutual information for all connectivity rates compared to low concentration. But when inhibition on the mushroom body is included, mutual information remains at high levels independent of other system parameters. This finding points to a pivotal role of inhibition in fly information processing without which the system's efficiency will be substantially reduced.

  11. Expert Diagnosing System for a Rotation Mechanism Based on a Neural Network

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    By combining the artificial neural network with the rule reasoning expert system,an expert diagnosing system for a rotation mechanism was established. This expert system takes advantage of both a neural network and a rule reasoning expert system; it can also make use of all kinds of knowledge in the repository to diagnose the fault with the positive and negative mixing reasoning mode. The binary system was adopted to denote all kinds of fault in a rotation mechanism. The neural networks were trained with a random parallel algorithm (Alopex). The expert system overcomes the self-learning difficulty of the rule reasoning expert system and the shortcoming of poor system control of the neural network.The expert system developed in this paper has powerful diagnosing ability.

  12. Neural network diagnostic system for dengue patients risk classification.

    Science.gov (United States)

    Faisal, Tarig; Taib, Mohd Nasir; Ibrahim, Fatimah

    2012-04-01

    With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75% prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7% prediction accuracy were achieved by using Levenberg-Marquardt algorithm.

  13. Inhibitory noise

    Directory of Open Access Journals (Sweden)

    Alain Destexhe

    2010-03-01

    Full Text Available Cortical neurons in vivo may operate in high-conductance states, in which the major part of the neuron's input conductance is due to synaptic activity, sometimes several-fold larger than the resting conductance. We examine here the contribution of inhibition in such high-conductance states. At the level of the absolute conductance values, several studies have shown that cortical neurons in vivo are characterized by strong inhibitory conductances. However, conductances are balanced and spiking activity is mostly determined by fluctuations, but not much is known about excitatory and inhibitory contributions to these fluctuations. Models and dynamic-clamp experiments show that, during high-conductance states, spikes are mainly determined by fluctuations of inhibition, or by inhibitory noise. This stands in contrast to low-conductance states, in which excitatory conductances determine spiking activity. To determine these contributions from experimental data, maximum likelihood methods can be designed and applied to intracellular recordings in vivo. Such methods indicate that action potentials are indeed mostly correlated with inhibitory fluctuations in awake animals. These results argue for a determinant role for inhibitory fluctuations in evoking spikes, and do not support feed-forward modes of processing, for which opposite patterns are predicted.

  14. DECOUPLING CONTROL OF TWO MOTORS SYSTEM BASED ON NEURAL NETWORK INVERSE SYSTEM

    Institute of Scientific and Technical Information of China (English)

    Wang Deming; Ju Ping; Liu Guohai

    2004-01-01

    In accordance with the characteristics of two motors system, the united mathematic model of two-motors inverter system with v/f variable frequency speed-regulating is given. Two-motor inverter system can be decoupled by the neural network invert system, and changed into a sub-system of speed and a sub-system of tension. Multiple controllers are designed, and good results are obtained. The system has good static and dynamic performances and high anti-disturbance of load.

  15. Excitatory and inhibitory synaptic mechanisms at the first stage of integration in the electroreception system of the shark.

    Science.gov (United States)

    Rotem, Naama; Sestieri, Emanuel; Hounsgaard, Jorn; Yarom, Yosef

    2014-01-01

    High impulse rate in afferent nerves is a common feature in many sensory systems that serve to accommodate a wide dynamic range. However, the first stage of integration should be endowed with specific properties that enable efficient handling of the incoming information. In elasmobranches, the afferent nerve originating from the ampullae of Lorenzini targets specific neurons located at the Dorsal Octavolateral Nucleus (DON), the first stage of integration in the electroreception system. Using intracellular recordings in an isolated brainstem preparation from the shark we analyze the properties of this afferent pathway. We found that stimulating the afferent nerve activates a mixture of excitatory and inhibitory synapses mediated by AMPA-like and GABAA receptors, respectively. The excitatory synapses that are extremely efficient in activating the postsynaptic neurons display unusual voltage dependence, enabling them to operate as a current source. The inhibitory input is powerful enough to completely eliminate the excitatory action of the afferent nerve but is ineffective regarding other excitatory inputs. These observations can be explained by the location and efficiency of the synapses. We conclude that the afferent nerve provides powerful and reliable excitatory input as well as a feed-forward inhibitory input, which is partially presynaptic in origin. These results question the cellular location within the DON where cancelation of expected incoming signals occurs.

  16. Excitatory and inhibitory synaptic mechanisms at the first stage of integration in the electroreception system of the shark

    Directory of Open Access Journals (Sweden)

    Naama eRotem

    2014-03-01

    Full Text Available High impulse rate in afferent nerves is a common feature in many sensory systems that serve to accommodate a wide dynamic range. However, the first stage of integration should be endowed with specific properties that enable efficient handling of the incoming information. In elasmobranches, the afferent nerve originating from the ampullae of Lorenzini targets specific neurons located at the Dorsal Octavolateral Nucleus (DON, the first stage of integration in the electroreception system. Using intracellular recordings in an isolated brainstem preparation from the shark we analyze the properties of this afferent pathway. We found that stimulating the afferent nerve activates a mixture of excitatory and inhibitory synapses mediated by AMPA-like and GABAA receptors, respectively. The excitatory synapses that are extremely efficient in activating the postsynaptic neurons display unusual voltage dependence, enabling them to operate as a current source. The inhibitory input is powerful enough to completely eliminate the excitatory action of the afferent nerve but is ineffective regarding other excitatory inputs. These observations can be explained by the location and efficiency of the synapses. We conclude that the afferent nerve provides powerful and reliable excitatory input as well as a feed-forward inhibitory input, which is partially presynaptic in origin. These results question the cellular location within the dorsal octavolateral nucleus where cancelation of expected incoming signals occurs.

  17. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  18. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview...

  19. Neural Networks Control of a Magnetic Levitation System

    Science.gov (United States)

    2001-04-17

    investigation of the use of artificial neural networks (ANN) in conjunction of proportional-integral-derivative ( PID ) controllers in control of non...neural networks in controlling closed-loop active magnetic bearing and comparison with the use of PID controllers . The obtained results should create a

  20. Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

    Science.gov (United States)

    Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

    2015-08-01

    A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

  1. 一类具有变时滞和脉冲的分层抑制细胞神经网络模型周期解的存在性%Existence of periodic solution for shunting inhibitory cellular neural netw orks w ith variable delays and impulses

    Institute of Scientific and Technical Information of China (English)

    佘连兵

    2015-01-01

    应用不等式技巧、Mawhin迭合度理论研究了带分布连续时滞和脉冲的SICNNs模型周期解的存在性,得到系统至少存在一个ω周期解的充分条件。最后,通过一个例子验证了结论的正确性。%T his paper is devoted to the global existence of one periodic solution for shunting inhibitory cellular neural networks (SICNNs) with time varying and continuously distributed delays and impulses by using inequality techniques and the Mawhin's continuation theorem ,a sufficient condition that the system there has at least a ω‐periodic solution is given . Finally , an example is provided to show the correctness of our analysis .

  2. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

    2010-07-01

    Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  3. Hybrid energy system evaluation in water supply system energy production: neural network approach

    Directory of Open Access Journals (Sweden)

    Fabio V. Goncalves, Helena M. Ramos, Luisa Fernanda R. Reis

    2010-01-01

    Full Text Available Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator – CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator – HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

  4. On the Computational Power of Spiking Neural P Systems with Self-Organization.

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-01-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

  5. On the Computational Power of Spiking Neural P Systems with Self-Organization

    Science.gov (United States)

    Wang, Xun; Song, Tao; Gong, Faming; Zheng, Pan

    2016-06-01

    Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

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

    Science.gov (United States)

    Wai, Rong-Jong; Lee, Jeng-Dao

    2008-01-01

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

  7. An alternative respiratory sounds classification system utilizing artificial neural networks

    Directory of Open Access Journals (Sweden)

    Rami J Oweis

    2015-04-01

    Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.

  8. Detection of Denial of Service Attacks against Domain Name System Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohd Fadlee A. Rasid

    2009-11-01

    Full Text Available In this paper we introduce an intrusion detection system for Denial of Service (DoS attacks against Domain Name System (DNS. Our system architecture consists of two most important parts: a statistical preprocessor and a neural network classifier. The preprocessor extracts required statistical features in a short-time frame from traffic received by the target name server. We compared three different neural networks for detecting and classifying different types of DoS attacks. The proposed system is evaluated in a simulated network and showed that the best performed neural network is a feed-forward backpropagation with an accuracy of 99%.

  9. Adaptive control of chaotic systems based on a single layer neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shen Liqun [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)

    2007-08-27

    This Letter presents an adaptive neural network control method for the chaos control problem. Based on a single layer neural network, the dynamic about the unstable fixed period point of the chaotic system can be adaptively identified without detailed information about the chaotic system. And the controlled chaotic system can be stabilized on the unstable fixed period orbit. Simulation results of Henon map and Lorenz system verify the effectiveness of the proposed control method.

  10. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  11. ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD

    Institute of Scientific and Technical Information of China (English)

    ZHURong-gang; JIANGChangsheng; FENGBin

    2004-01-01

    A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.

  12. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  13. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  14. Application of Neural network PID Controller in Constant Temperature and Constant Liquid-level System

    Institute of Scientific and Technical Information of China (English)

    Chen,Guochu; Zhang,Lin; Hao,Ninmei; Liu,Xianguang; Wang,Junhong

    2003-01-01

    Guided by the principle of neural network, an intelligent PID controller based on neural network is devised and applied to control of constant temperature and constant liquidlevel system. The experiment results show that this controller has high accuracy and strong robustness and good characters.

  15. Synthetical Control of AGC/LPC System Based on Neural Networks Internal Model Control

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.

  16. Review: the role of neural crest cells in the endocrine system.

    Science.gov (United States)

    Adams, Meghan Sara; Bronner-Fraser, Marianne

    2009-01-01

    The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.

  17. 具分布时滞和脉冲的Cohen-Grossberg SICNNs的概周期解%Almost Periodic Solutions for Cohen-Grossberg Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Impulses

    Institute of Scientific and Technical Information of China (English)

    农秀丽; 杨莉

    2014-01-01

    研究一类具分布时滞和脉冲的Cohen-Grossberg SICNNs模型。利用不动点定理,得到一些保证所考虑模型存在概周期解的充分条件,并举例说明了所得结果的可行性。%In this paper, a class of Cohen-Grossberg Shunting Inhibitory cellular neural net-works with distributed delays and impulses are considered. Some criteria for the exis-tence of nonzero almost period⁃ic solutions are established by Banach fixed point theorem.Moreover, an example is employed to illus⁃trate our feasible results.

  18. FGF Signaling Transforms Non-neural Ectoderm into Neural Crest

    OpenAIRE

    Yardley, Nathan; García-Castro, Martín I.

    2012-01-01

    The neural crest arises at the border between the neural plate and the adjacent non-neural ectoderm. It has been suggested that both neural and non-neural ectoderm can contribute to the neural crest. Several studies have examined the molecular mechanisms that regulate neural crest induction in neuralized tissues or the neural plate border. Here, using the chick as a model system, we address the molecular mechanisms by which non-neural ectoderm generates neural crest. We report that in respons...

  19. A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper,an adaptive dynamic control scheme based on a fuzzy neural network is presented,that presents utilizes both feed-forward and feedback controller elements.The former of the two elements comprises a neural network with both identification and control role,and the latter is a fuzzy neural algorithm,which is introduced to provide additional control enhancement.The feedforward controller provides only coarse control,whereas the feedback oontroller can generate on-line conditional proposition rule automatically to improve the overall control action.These properties make the design very versatile and applicable to a range of industrial applications.

  20. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  1. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

  2. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  3. Artificial Neural Network-Based System for PET Volume Segmentation.

    Science.gov (United States)

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  4. A Tool for Fast Development of Modular and Hierarchic Neural Network-based Systems

    Directory of Open Access Journals (Sweden)

    Francisco Reinaldo

    2006-08-01

    Full Text Available This paper presents PyramidNet tool as a fast and easy way to develop Modular and Hierarchic Neural Network-based Systems. This tool facilitates the fast emergence of autonomous behaviors in agents because it uses a hierarchic and modular control methodology of heterogeneous learning modules: the pyramid. Using the graphical resources of PyramidNet the user is able to specify a behavior system even having little understanding of artificial neural networks. Experimental tests have shown that a very significant speedup is attained in the development of modular and hierarchic neural network-based systems by using this tool.

  5. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training...... samples from the system, after which linearized state space models are extracted from the neural network in a number of operating points according to a simple and computationally cheap scheme. Robust observer-based controllers can then be designed in each of these operating points,and gain scheduling...

  6. An Expert System Using A Neural Network For Steam Generator Tube Inspection

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Kilyoo; Huh, Younghwan [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Woo, Heegon; Choi, Sungsoo [Korea Electric Power Corporation, Daejeon (Korea, Republic of)

    1991-04-15

    An expert system using neural network is built to automatically evaluate eddy current (EC) signals generated during steam generator (S/G) tubes inspection. The system consists of three subsystem, i.e., syntactic pattern recognition subsystem, neural network subsystem and rule based production subsystem. The syntactic pattern recognition subsystem makes it easy to process the vast EC signal data, screens EC signals and detects event signals such as defect signals and structural signals. The neural network subsystem is useful to classify the event signals which often contain noise signals. The expert system implemented on HP 9000/370 workstation also supplies a good EC test data management function.

  7. Intelligent Intrusion Detection System Model Using Rough Neural Network

    Institute of Scientific and Technical Information of China (English)

    YAN Huai-zhi; HU Chang-zhen; TAN Hui-min

    2005-01-01

    A model of intelligent intrusion detection based on rough neural network (RNN), which combines the neural network and rough set, is presented. It works by capturing network packets to identify network intrusions or malicious attacks using RNN with sub-nets. The sub-net is constructed by detection-oriented signatures extracted using rough set theory to detect different intrusions. It is proved that RNN detection method has the merits of adaptive, high universality,high convergence speed, easy upgrading and management.

  8. Radial basis function neural network for power system load-flow

    Energy Technology Data Exchange (ETDEWEB)

    Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)

    2008-01-15

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  9. Compensating for Channel Fading in DS-CDMA Communication Systems Employing ICA Neural Network Detectors

    Directory of Open Access Journals (Sweden)

    David Overbye

    2005-06-01

    Full Text Available In this paper we examine the impact of channel fading on the bit error rate of a DS-CDMA communication system. The system employs detectors that incorporate neural networks effecting methods of independent component analysis (ICA, subspace estimation of channel noise, and Hopfield type neural networks. The Rayleigh fading channel model is used. When employed in a Rayleigh fading environment, the ICA neural network detectors that give superior performance in a flat fading channel did not retain this superior performance. We then present a new method of compensating for channel fading based on the incorporation of priors in the ICA neural network learning algorithms. When the ICA neural network detectors were compensated using the incorporation of priors, they give significantly better performance than the traditional detectors and the uncompensated ICA detectors. Keywords: CDMA, Multi-user Detection, Rayleigh Fading, Multipath Detection, Independent Component Analysis, Prior Probability Hebbian Learning, Natural Gradient

  10. A new neural network model for the feedback stabilization of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Mei-qin LIU; Sen-lin ZHANG; Gang-long YAN

    2008-01-01

    A new neural network model termed 'standard neural network model' (SNNM) is presented,and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system.The control design constraints are shown to be a set of linear matrix inequalities (LMIs),which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law.Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM.Finally,three numerical examples are provided to illustrate the design developed in this paper.

  11. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  12. A hyperstable neural network for the modelling and control of nonlinear systems

    Indian Academy of Sciences (India)

    K Warwick; Q M Zhu; Z Ma

    2000-04-01

    A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

  13. Sleep bruxism is related to decreased inhibitory control of trigeminal motoneurons, but not with reticulobulbar system.

    Science.gov (United States)

    İnan, Rahşan; Şenel, Gülçin Benbir; Yavlal, Figen; Karadeniz, Derya; Gündüz, Ayşegül; Kızıltan, Meral E

    2017-01-01

    Sleep bruxism (SB) is a stereotyped movement disorder characterized by grinding or clenching of the teeth during sleep. We aimed to understand the abnormal networks related to the excitability of masticatory pathways in patients with SB. Eleven patients with SB and age- and gender-matched 20 healthy subjects were prospectively enrolled in our study. The masseter inhibitory reflex (MIR) after electrical stimulation and auditory startle reaction (ASR) were examined. For MIR responses, durations of early and late silent period (SP) were shorter and the degree of suppression of SPs was significantly lower in SB group in comparison to those obtained in healthy subjects. The ASR responses even of the masseter muscle, however, were similar between patients with SB and healthy individuals. Abnormal MIR provides support for the decreased inhibitory control of the central masticatory circuits in SB whereas normal ASR suggests the integrity and normal functioning of brainstem pathways mediating startle reaction. Although the sample size is small, our results are in line with previous findings and suggest an abnormally decreased inhibition in trigeminal motoneurons to masseter muscle rather than reticulobulbar pathways in patients with SB.

  14. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  15. Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

    2006-07-01

    An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

  16. Transitions between beta and gamma rhythms in neural systems

    DEFF Research Database (Denmark)

    Sosnovtseva, Olga; Setsinsky, D; Fausbøll, Anders;

    2002-01-01

    We study the coexistence of different rhythms in a local network of one inhibitory and two excitatory nerve cells for a wide range of the excitatory synapse strength and of the slow K+-channel conductance. The dynamic features of spike trains in the presence of noise are discussed. It is found th...

  17. Neural cell adhesion molecule-180-mediated homophilic binding induces epidermal growth factor receptor (EGFR) down-regulation and uncouples the inhibitory function of EGFR in neurite outgrowth

    DEFF Research Database (Denmark)

    Povlsen, Gro Klitgaard; Berezin, Vladimir; Bock, Elisabeth

    2008-01-01

    The neural cell adhesion molecule (NCAM) plays important roles in neuronal development, regeneration, and synaptic plasticity. NCAM homophilic binding mediates cell adhesion and induces intracellular signals, in which the fibroblast growth factor receptor plays a prominent role. Recent studies...... not require NCAM-mediated fibroblast growth factor receptor activation....... on axon guidance in Drosophila suggest that NCAM also regulates the epidermal growth factor receptor (EGFR) (Molecular and Cellular Neuroscience, 28, 2005, 141). A possible interaction between NCAM and EGFR in mammalian cells has not been investigated. The present study demonstrates for the first time...

  18. Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems.

    Science.gov (United States)

    Wang, Min; Chen, Bing; Shi, Peng

    2008-06-01

    This paper proposes a novel adaptive neural control scheme for a class of perturbed strict-feedback nonlinear time-delay systems with unknown virtual control coefficients. Based on the radial basis function neural network online approximation capability, an adaptive neural controller is presented by combining the backstepping approach and Lyapunov-Krasovskii functionals. The proposed controller guarantees the semiglobal boundedness of all the signals in the closed-loop system and contains minimal learning parameters. Finally, three simulation examples are given to demonstrate the effectiveness and applicability of the proposed scheme.

  19. Design of Neural Network Control System for Controlling Trajectory of Autonomous Underwater Vehicles

    Directory of Open Access Journals (Sweden)

    İkbal Eski

    2014-01-01

    Full Text Available A neural network based robust control system design for the trajectory of Autonomous Underwater Vehicles (AUVs is presented in this paper. Two types of control structure were used to control prescribed trajectories of an AUV. The vehicle was tested with random disturbances while taxiing under water. The results of the simulation showed that the proposed neural network based robust control system has superior performance in adapting to large random disturbances such as underwater flow. It is proved that this kind of neural predictor could be used in real-time AUV applications.

  20. Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

    Institute of Scientific and Technical Information of China (English)

    Mou Chen; Chang-Sheng Jiang; Qing-Xian Wu

    2008-01-01

    In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

  1. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Document Server

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  2. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Document Server

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  3. 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

    2016-03-17

    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.

  4. Simulation of Missile Autopilot with Two-Rate Hybrid Neural Network System

    Directory of Open Access Journals (Sweden)

    ASTROV, I.

    2007-04-01

    Full Text Available This paper proposes a two-rate hybrid neural network system, which consists of two artificial neural network subsystems. These neural network subsystems are used as the dynamic subsystems controllers.1 This is because such neuromorphic controllers are especially suitable to control complex systems. An illustrative example - two-rate neural network hybrid control of decomposed stochastic model of a rigid guided missile over different operating conditions - was carried out using the proposed two-rate state-space decomposition technique. This example demonstrates that this research technique results in simplified low-order autonomous control subsystems with various speeds of actuation, and shows the quality of the proposed technique. The obtained results show that the control tasks for the autonomous subsystems can be solved more qualitatively than for the original system. The simulation and animation results with use of software package Simulink demonstrate that this research technique would work for real-time stochastic systems.

  5. A configurable realtime DWT-based neural data compression and communication VLSI system for wireless implants.

    Science.gov (United States)

    Yang, Yuning; Kamboh, Awais M; Mason, Andrew J

    2014-04-30

    This paper presents the design of a complete multi-channel neural recording compression and communication system for wireless implants that addresses the challenging simultaneous requirements for low power, high bandwidth and error-free communication. The compression engine implements discrete wavelet transform (DWT) and run length encoding schemes and offers a practical data compression solution that faithfully preserves neural information. The communication engine encodes data and commands separately into custom-designed packet structures utilizing a protocol capable of error handling. VLSI hardware implementation of these functions, within the design constraints of a 32-channel neural compression implant, is presented. Designed in 0.13μm CMOS, the core of the neural compression and communication chip occupies only 1.21mm(2) and consumes 800μW of power (25μW per channel at 26KS/s) demonstrating an effective solution for intra-cortical neural interfaces.

  6. A chaotic neural network mimicking an olfactory system and its application on image recognition

    Institute of Scientific and Technical Information of China (English)

    WANG Le; LI Guang; LI Xu; GUO Hong-ji; Walter J. Freeman

    2004-01-01

    Based on the research of a biological olfactory system, a novel chaotic neural network model - K set model has been established. This chaotic neural network not only simulates the real brain activity of an olfactory system, but also presents a novel chaotic concept for signal processing and pattern recognition. The characteristics of the K set models are investigated and show that a KⅢ model can be used for image pattern classification.

  7. An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End

    OpenAIRE

    Lee, Seung Bae; Lee, Byunghun; Kiani, Mehdi; Mahmoudi, Babak; Gross, Robert; Ghovanloo, Maysam

    2015-01-01

    An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the D...

  8. A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

    OpenAIRE

    Yong Tao; Jiaqi Zheng; Yuanchang Lin

    2016-01-01

    A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

  9. Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ferreira, Wagner Peron; Silveira, Maria do Carmo G.; Lotufo, AnnaDiva P.; Minussi, Carlos. R. [Department of Electrical Engineering, Sao Paulo State University (UNESP), P.O. Box 31, 15385-000, Ilha Solteira, SP (Brazil)

    2006-04-15

    This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (author)

  10. Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

    2014-03-01

    Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

  11. Neural networks for structural design - An integrated system implementation

    Science.gov (United States)

    Berke, Laszlo; Hafez, Wassim; Pao, Yoh-Han

    1992-01-01

    The development of powerful automated procedures to aid the creative designer is becoming increasingly critical for complex design tasks. In the work described here Artificial Neural Nets are applied to acquire structural analysis and optimization domain expertise. Based on initial instructions from the user an automated procedure generates random instances of structural analysis and/or optimization 'experiences' that cover a desired domain. It extracts training patterns from the created instances, constructs and trains an appropriate network architecture and checks the accuracy of net predictions. The final product is a trained neural net that can estimate analysis and/or optimization results instantaneously.

  12. Automated implementation of rule-based expert systems with neural networks for time-critical applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song; Govind, Girish

    1991-01-01

    In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

  13. An Active Stereo Vision System Based on Neural Pathways of Human Binocular Motor System

    Institute of Scientific and Technical Information of China (English)

    Yu-zhang Gu; Makoto Sato; Xiao-lin Zhang

    2007-01-01

    An active stereo vision system based on a model of neural pathways of human binocular motor system is proposed. With this model, it is guaranteed that the two cameras of the active stereo vision system can keep their lines of sight fixed on the same target object during smooth pursuit. This feature is very important for active stereo vision systems, since not only 3D reconstruction needs the two cameras have an overlapping field of vision, but also it can facilitate the 3D reconstruction algorithm. To evaluate the effectiveness of the proposed method, some software simulations are done to demonstrate the same target tracking characteristic in a virtual environment apt to mistracking easily. Here, mistracking means two eyes track two different objects separately. Then the proposed method is implemented in our active stereo vision system to perform real tracking task in a laboratory scene where several persons walk self-determining. Before the proposed model is implemented in the system, mistracking occurred frequently. After it is enabled, mistracking never occurred. The result shows that the vision system based on neural pathways of human binocular motor system can reliably avoid mistracking.

  14. General and Food-Specific Inhibitory Control As Moderators of the Effects of the Impulsive Systems on Food Choices.

    Science.gov (United States)

    Zhang, Xuemeng; Chen, Shuaiyu; Chen, Hong; Gu, Yan; Xu, Wenjian

    2017-01-01

    The present study aimed to extend the application of the reflective-impulsive model to restrained eating and explore the effect of automatic attention (impulsive system) on food choices. Furthermore, we examined the moderating effects of general inhibitory control (G-IC) and food-specific inhibitory control (F-IC) on successful and unsuccessful restrained eaters (US-REs). Automatic attention was measured using "the EyeLink 1000," which tracked eye movements during the process of making food choices, and G-IC and F-IC were measured using the Stop-Signal Task. The results showed that food choices were related to automatic attention and that G-IC and F-IC moderated the predictive relationship between automatic attention and food choices. Furthermore, among successful restrained eaters (S-REs), automatic attention to high caloric foods did not predict food choices, regardless of whether G-IC or F-IC was high or low. Whereas food choice was positively correlated with automatic attention among US-REs with poor F-IC, this pattern was not observed in those with poor G-IC. In conclusion, the S-REs had more effective self-management skills and their food choices were affected less by automatic attention and inhibitory control. Unsuccessful restrained eating was associated with poor F-IC (not G-IC) and greater automatic attention to high caloric foods. Thus, clinical interventions should focus on enhancing F-IC, not G-IC, and on reducing automatic attention to high caloric foods.

  15. General and Food-Specific Inhibitory Control As Moderators of the Effects of the Impulsive Systems on Food Choices

    Directory of Open Access Journals (Sweden)

    Xuemeng Zhang

    2017-05-01

    Full Text Available The present study aimed to extend the application of the reflective-impulsive model to restrained eating and explore the effect of automatic attention (impulsive system on food choices. Furthermore, we examined the moderating effects of general inhibitory control (G-IC and food-specific inhibitory control (F-IC on successful and unsuccessful restrained eaters (US-REs. Automatic attention was measured using “the EyeLink 1000,” which tracked eye movements during the process of making food choices, and G-IC and F-IC were measured using the Stop-Signal Task. The results showed that food choices were related to automatic attention and that G-IC and F-IC moderated the predictive relationship between automatic attention and food choices. Furthermore, among successful restrained eaters (S-REs, automatic attention to high caloric foods did not predict food choices, regardless of whether G-IC or F-IC was high or low. Whereas food choice was positively correlated with automatic attention among US-REs with poor F-IC, this pattern was not observed in those with poor G-IC. In conclusion, the S-REs had more effective self-management skills and their food choices were affected less by automatic attention and inhibitory control. Unsuccessful restrained eating was associated with poor F-IC (not G-IC and greater automatic attention to high caloric foods. Thus, clinical interventions should focus on enhancing F-IC, not G-IC, and on reducing automatic attention to high caloric foods.

  16. NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Tian Sheping; Ding Guoqing; Yan Detian; Lin Liangming

    2004-01-01

    The pneumatic artificial muscles are widely used in the fields of medical robots,etc.Neural networks are applied to modeling and controlling of artificial muscle system.A single-joint artificial muscle test system is designed.The recursive prediction error (RPE) algorithm which yields faster convergence than back propagation (BP) algorithm is applied to train the neural networks.The realization of RPE algorithm is given.The difference of modeling of artificial muscles using neural networks with different input nodes and different hidden layer nodes is discussed.On this basis the nonlinear control scheme using neural networks for artificial muscle system has been introduced.The experimental results show that the nonlinear control scheme yields faster response and higher control accuracy than the traditional linear control scheme.

  17. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

  18. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  19. Neural network decoupling technique and its application to a powered wheelchair system.

    Science.gov (United States)

    Tuan Nghia Nguyen; Nguyen, Hung T

    2015-08-01

    This paper proposes a neural network decoupling technique for an uncertain multivariable system. Based on a linear diagonalization technique, a reference model is designed using nominal parameters to provide training signals for a neural network decoupler. A neural network model is designed to learn the dynamics of the uncertain multivariable system in order to avoid required calculations of the plant Jacobian. To avoid overfitting problem, both neural networks are trained by the Lavenberg-Marquardt with Bayesian regulation algorithm that uses a real-time recurrent learning algorithm to obtain gradient information. Three experimental results in the powered wheelchair control application confirm that the proposed technique effectively minimises the coupling effects caused by input-output interactions even under the condition of system uncertainties.

  20. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  1. Diagnosis of mechanical pumping system using neural networks and system parameters analysis

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

    2009-01-15

    Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

  2. Integrating resource defence theory with a neural nonapeptide pathway to explain territory-based mating systems.

    Science.gov (United States)

    Oldfield, Ronald G; Harris, Rayna M; Hofmann, Hans A

    2015-01-01

    The ultimate-level factors that drive the evolution of mating systems have been well studied, but an evolutionarily conserved neural mechanism involved in shaping behaviour and social organization across species has remained elusive. Here, we review studies that have investigated the role of neural arginine vasopressin (AVP), vasotocin (AVT), and their receptor V1a in mediating variation in territorial behaviour. First, we discuss how aggression and territoriality are a function of population density in an inverted-U relationship according to resource defence theory, and how territoriality influences some mating systems. Next, we find that neural AVP, AVT, and V1a expression, especially in one particular neural circuit involving the lateral septum of the forebrain, are associated with territorial behaviour in males of diverse species, most likely due to their role in enhancing social cognition. Then we review studies that examined multiple species and find that neural AVP, AVT, and V1a expression is associated with territory size in mammals and fishes. Because territoriality plays an important role in shaping mating systems in many species, we present the idea that neural AVP, AVT, and V1a expression that is selected to mediate territory size may also influence the evolution of different mating systems. Future research that interprets proximate-level neuro-molecular mechanisms in the context of ultimate-level ecological theory may provide deep insight into the brain-behaviour relationships that underlie the diversity of social organization and mating systems seen across the animal kingdom.

  3. AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

    Institute of Scientific and Technical Information of China (English)

    JIA Li; YU Jinshou

    2005-01-01

    In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.

  4. Multi-channel holographic birfurcative neural network system for real-time adaptive EOS data analysis

    Science.gov (United States)

    Liu, Hua-Kuang; Diep, J.; Huang, K.

    1991-01-01

    Viewgraphs on multi-channel holographic bifurcative neural network system for real-time adaptive Earth Observing System (EOS) data analysis are presented. The objective is to research and develop an optical bifurcating neuromorphic pattern recognition system for making optical data array comparisons and to evaluate the use of the system for EOS data classification, reduction, analysis, and other applications.

  5. Study of a Bionic Pattern Classifier Based on Olfactory Neural System

    Institute of Scientific and Technical Information of China (English)

    Xu Li; Guang Li; Le Wang; Walter J.Freeman

    2004-01-01

    Simulating biological olfactory neural system, KⅢ network, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the KⅢ network works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting nmerals are presented here. The classification performance of the KⅢ network at different noise levels was also investigated.

  6. Neural network-based H∞ filtering for nonlinear systems with time-delays

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.

  7. Research on a Neural Network Approach Based Diagnosis Expert System of Crack Control in Massive Concrete

    Institute of Scientific and Technical Information of China (English)

    HAN Liu-xin; WANG Huan-chen; ZHANG Xian-hui

    2001-01-01

    A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and selfstudy is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.

  8. A Worsted Yarn Virtual Production System Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    董奎勇; 于伟东

    2004-01-01

    Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.

  9. THE ROLE OF BRAIN-STEM DISCENDING INHIBITORY SYSTEM IN THE ANTINOCICEPTIVE EFFECT ELICITED BY MUSCLE SPINDLE AFFERENTS

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Objective To analyse the antinociceptive effect of muscle spindle afferents and the involved mechanism.Methods The single unit of wide dynamic range(WDR) neurons in the spinal cord dorsal horn were recorded extracelluarly.The effects of muscle spindle afferents elicited by intravenous administration of succinylcholine (Sch) on nociceptive responses (C-fibres-evoked responses,C-responses) of WDR neurons were observed before and after bilateral lesions of ventrolateral periaqueduct gray (PAG).And the effects of muscle spindle afferents on the spontaneous discharge of the tail-flick related cell in the rostral ventro medial medulla (RVM) and on the spontaneous discharge of the PAG neurons were observed.Results The C-responses of WDR neurons were significantly inhibited by muscle spindle afferents,and the inhibitory effects were reduced by bilateral lesions of ventrolateral PAG.The spontaneous discharge of the off-cell in the RVM was excited while the on-cell was inhibited by intravenous administration of Sch.The spontaneous discharge of the PAG neurons were excited by muscle spindle afferents.Conclusion Muscle spindle afferents show a distinct effect of antinociception.PAG-RVM descending inhibitory system may play an important role in this nociceptive modulative mechanism.

  10. Improvement of Power System Stability using Artificial Neural Network based HVDC Controls

    Directory of Open Access Journals (Sweden)

    Nagu Bhookya

    2013-06-01

    Full Text Available In this paper, investigation is carried out for the improvement of power system stability by utilizing auxiliary controls for controlling HVDC power flow. The current controller model and the line dynamics are considered in the stability analysis. Transient stability analysis is done on a multi-machine system, where, a neural network controller is developed to improve the stability of the power system and to improve the response time of the controller to the changing conditions in power system. The results show the application of the neural network controller in AC-DC power systems.

  11. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  12. Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.

    Science.gov (United States)

    Castañeda, Carlos E; Esquivel, P

    2012-07-01

    A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.

  13. Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Shao-Cheng Tong; Yong-Ming Li

    2009-01-01

    In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy-neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed rccursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

  14. Developing and using expert systems and neural networks in medicine: a review on benefits and challenges.

    Science.gov (United States)

    Sheikhtaheri, Abbas; Sadoughi, Farahnaz; Hashemi Dehaghi, Zahra

    2014-09-01

    Complicacy of clinical decisions justifies utilization of information systems such as artificial intelligence (e.g. expert systems and neural networks) to achieve better decisions, however, application of these systems in the medical domain faces some challenges. We aimed at to review the applications of these systems in the medical domain and discuss about such challenges. Following a brief introduction of expert systems and neural networks by representing few examples, the challenges of these systems in the medical domain are discussed. We found that the applications of expert systems and artificial neural networks have been increased in the medical domain. These systems have shown many advantages such as utilization of experts' knowledge, gaining rare knowledge, more time for assessment of the decision, more consistent decisions, and shorter decision-making process. In spite of all these advantages, there are challenges ahead of developing and using such systems including maintenance, required experts, inputting patients' data into the system, problems for knowledge acquisition, problems in modeling medical knowledge, evaluation and validation of system performance, wrong recommendations and responsibility, limited domains of such systems and necessity of integrating such systems into the routine work flows. We concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.

  15. A Neural Network with Minimal Structure for Maglev System Modeling and Control

    OpenAIRE

    1999-01-01

    6 pages; International audience; The paper is concerned with the determination of a minimal structure of a one hidden layer perceptron for system identification and control. Structural identification is a key issue in neural modeling. Decreasing the size of the neural networks is a way to avoid overfitting and bad generalization and leads moreover to simpler models which are required for real time applications, particularly in control. A learning algorithm and a pruning method both based on a...

  16. MINUTIAE EXTRACTION BASED ON ARTIFICIAL NEURAL NETWORKS FOR AUTOMATIC FINGERPRINT RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Necla ÖZKAYA

    2007-01-01

    Full Text Available Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships. Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images. In this study, a new approach based on artificial neural networks to extract minutiae from fingerprint images is developed and introduced. The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.

  17. The frog vestibular system as a model for lesion-induced plasticity: basic neural principles and implications for posture control

    Directory of Open Access Journals (Sweden)

    Francois M Lambert

    2012-04-01

    Full Text Available Studies of behavioral consequences after unilateral labyrinthectomy have a long tradition in the quest of determining rules and limitations of the CNS to exert plastic changes that assist the recuperation from the loss of sensory inputs. Frogs were among the first animal models to illustrate general principles of regenerative capacity and reorganizational neural flexibility after a vestibular lesion. The continuous successful use of the latter animals is in part based on the easy access and identifiability of nerve branches to inner ear organs for surgical intervention, the possibility to employ whole brain preparations for in vitro studies and the limited degree of freedom of postural reflexes for quantification of behavioral impairments and subsequent improvements. Major discoveries that increased the knowledge of post-lesional reactive mechanisms in the central nervous system include alterations in vestibular commissural signal processing and activation of cooperative changes in excitatory and inhibitory inputs to disfacilitated neurons. Moreover, the observed increase of synaptic efficacy in propriospinal circuits illustrates the importance of limb proprioceptive inputs for postural recovery. Accumulated evidence suggests that the lesion-induced neural plasticity is not a goal-directed process that aims towards a meaningful restoration of vestibular reflexes but rather attempts a survival of those neurons that have lost their excitatory inputs. Accordingly, the reaction mechanism causes an improvement of some components but also a deterioration of other aspects as seen by spatio-temporally inappropriate vestibulo-motor responses, similar to the consequences of plasticity processes in various sensory systems and species. The generality of the findings indicate that frogs continue to form a highly amenable vertebrate model system for exploring molecular and physiological events during cellular and network reorganization after a loss of

  18. Psychological Processing in Chronic Pain: A Neural Systems Approach

    Science.gov (United States)

    Simons, Laura; Elman, Igor; Borsook, David

    2014-01-01

    Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementation of psychology-based treatments may be better understood. In this review we evaluate some of the principle processes that may be altered as a consequence of chronic pain in the context of localized and integrated neural networks. These changes are ongoing, vary in their magnitude, and their hierarchical manifestations, and may be temporally and sequentially altered by treatments, and all contribute to an overall pain phenotype. Furthermore, we link altered psychological processes to specific evidence-based treatments to put forth a model of pain neuroscience psychology. PMID:24374383

  19. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  20. 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.

  1. Early-life Social Isolation Impairs the Gonadotropin-Inhibitory Hormone Neuronal Activity and Serotonergic System in Male Rats

    Directory of Open Access Journals (Sweden)

    Tomoko eSoga

    2015-11-01

    Full Text Available Social isolation in early life deregulates the serotonergic system of the brain, compromising reproductive function. Gonadotropin-inhibitory hormone (GnIH neurons in the dorsomedial hypothalamic nucleus are critical to the inhibitory regulation of gonadotropin-releasing hormone neuronal activity in the brain and release of luteinising hormone by the pituitary gland. Although GnIH responds to stress, the role of GnIH in social isolation-induced deregulation of the serotonin system and reproductive function remains unclear. We investigated the effect of social isolation in early life on the serotonergic–GnIH neuronal system using enhanced green fluorescent protein (EGFP-tagged GnIH-transgenic rats. Socially isolated rats were observed for anxious and depressive behaviours. Using immunohistochemistry, we examined c-Fos protein expression in EGFP–GnIH neurons in 9-week-old adult male rats after 6 weeks post-weaning isolation or group -housing. We also inspected serotonergic fibre juxtapositions in EGFP–GnIH neurons in control and socially isolated male rats. Socially isolated rats exhibited anxious and depressive behaviours. The total number of EGFP–GnIH neurons was the same in control and socially isolated rats, but c-Fos expression in GnIH neurons was significantly reduced in socially isolated rats. Serotonin fibre juxtapositions on EGFP–GnIH neurons was also lower in socially isolated rats. In addition, levels of tryptophan hydroxylase mRNA expression in the dorsal raphe nucleus were significantly attenuated in these rats. These results suggest that social isolation in early life results in lower serotonin levels, which reduce GnIH neuronal activity and may lead to reproductive failure.

  2. Evaluation of fungicides enestroburin and SYP1620 on their inhibitory activities to fungi and oomycetes and systemic translocation in plants.

    Science.gov (United States)

    Liu, Pengfei; Wang, Haiqiang; Zhou, Yuxin; Meng, Qingxiao; Si, Naiguo; Hao, Jianjun J; Liu, Xili

    2014-06-01

    Enestroburin and SYP1620 are newly developed strobilurin chemicals carrying fungicidal activity and need to be fully characterized in activities of anti-oomycete or anti-fungi, disease prevention and systemic translocation in planta. Their inhibitory activities were examined by amending the chemical in agar media, on which selected plant pathogens were grown and mycelial growth were measured. Effective concentrations for 50% inhibition (EC50) of mycelial growth were calculated to determine the level of fungicide sensitivity of the pathogen. Azoxystrobin was used as control. To examine the prevention and systemic translocation in plants, the fungicides were either sprayed on wheat leaves or dipped on wheat roots, which then were detected using high performance liquid chromatography. All the three fungicides inhibited mycelial growth of Sphacelotheca reiliana, Phytophthora infestans, Peronophythora litchi, and Magnaporthe oryzae, with EC50 values ranging from 0.02 to 2.84μg/ml; EC50 of SYP1620 was significantly lower than that of azoxystrobin and enestroburin on Valsa mali, Gaeumannomyces graminis, Alternaria solani, and Colletotrichun orbiculare. The three QoI fungicides showed strong inhibitory activities on spore germination against the 13 pathogens tested and were highly effective on biotrophic pathogens tested. Enestroburin and SYP1620 penetrated and spread in wheat leaves, but the penetration and translocation levels were lower compared to azoxystrobin. The three fungicides were all rapidly taken up by wheat roots and transported upwards, with greater fungicide concentrations in roots than in stems and leaves. The results indicate that enestroburin and SYP1620 are systemic fungicides that inhibit a broad spectrum of fungi and oomycetes.

  3. NEURAL NETWORK SYSTEM TRACKS LANDING AIRCRAFT WHEN WIND DIRECTION CHANGES

    Directory of Open Access Journals (Sweden)

    G. N. Lebedev

    2015-01-01

    Full Text Available The article describes the problem of solving important practical problems of redistribution of aircraft when landing on a different route of the Moscow air hub in the event of a sudden change of weather conditions. The neural network procedure to set priorities for each aircraft in real time based on dynamic programming is offered. This allowed us to generate lists of vessels for each trace landing and to determine their priority for landing.

  4. Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network

    Institute of Scientific and Technical Information of China (English)

    JIA Chun-yu; LIU Hong-min; ZHOU Hui-feng

    2005-01-01

    For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully,and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy.

  5. Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

    CERN Document Server

    Pradeep, J; Himavathi, S; 10.5121/ijcsit.2011.3103

    2011-01-01

    An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.

  6. A quick overview of membrane computing with some details about spiking neural P systems

    Institute of Scientific and Technical Information of China (English)

    Gheorghe Pǎun

    2007-01-01

    We briefly present the basic elements of membrane computing,a branch of natural computing inspired by the structure and functioning of living cells,then we give some details about spiking neural P systems,a class of membrane systems recently introduced,with motivations related to the way neurons communicate by means of spikes.In both cases,of general P systems and of spiking neural P systems,we introduce the fundamental concepts,give a few examples,then recall the types of results and of applications.A series of bibliographical references are provided.

  7. Fuzzy Control Based on Neural Networks for Armored Vehicle Electric Drive System

    Institute of Scientific and Technical Information of China (English)

    MA Xiao-jun; LI Hua; ZHANG Jian; ZHANG Yu-nan

    2006-01-01

    In order to meet rigorous demands of control of electric motors in armored vehicle electric drive system and make the system of strong robustness and antijamming capability, a fuzzy control method based on neural networks is put forward. The simulation model of the armored vehicle electric drive system is built up to test the validity of the control. Simulation experiments show that when load is increased or decreased suddenly, the system adopting fuzzy control based on neural networks is insensitive to parameter change and has little overshooting and oscillation compared with PID control.

  8. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  9. GA-BASED PID NEURAL NETVVORK CONTROL FOR MAGNETIC BEARING SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    LI Guodong; ZHANG Qingchun; LIANG Yingchun

    2007-01-01

    In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA(genetic algorithm)-based PID neural network controller is designed and trained to emulate the operation of a complete system (magnetic beating, controller, and power amplifiers).The feasibility of using a neural network to control nonlinear magnetic beating systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions (chromosomes), increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

  10. An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

    Science.gov (United States)

    Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

    2015-07-01

    We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

  11. An analysis of image storage systems for scalable training of deep neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Seung-Hwan [ORNL; Young, Steven R [ORNL; Patton, Robert M [ORNL

    2016-01-01

    This study presents a principled empirical evaluation of image storage systems for training deep neural networks. We employ the Caffe deep learning framework to train neural network models for three different data sets, MNIST, CIFAR-10, and ImageNet. While training the models, we evaluate five different options to retrieve training image data: (1) PNG-formatted image files on local file system; (2) pushing pixel arrays from image files into a single HDF5 file on local file system; (3) in-memory arrays to hold the pixel arrays in Python and C++; (4) loading the training data into LevelDB, a log-structured merge tree based key-value storage; and (5) loading the training data into LMDB, a B+tree based key-value storage. The experimental results quantitatively highlight the disadvantage of using normal image files on local file systems to train deep neural networks and demonstrate reliable performance with key-value storage based storage systems. When training a model on the ImageNet dataset, the image file option was more than 17 times slower than the key-value storage option. Along with measurements on training time, this study provides in-depth analysis on the cause of performance advantages/disadvantages of each back-end to train deep neural networks. We envision the provided measurements and analysis will shed light on the optimal way to architect systems for training neural networks in a scalable manner.

  12. Model algorithm control using neural networks for input delayed nonlinear control system

    Institute of Scientific and Technical Information of China (English)

    Yuanliang Zhang; Kil To Chong

    2015-01-01

    The performance of the model algorithm control method is partial y based on the accuracy of the system’s model. It is diffi-cult to obtain a good model of a nonlinear system, especial y when the nonlinearity is high. Neural networks have the ability to“learn”the characteristics of a system through nonlinear mapping to rep-resent nonlinear functions as wel as their inverse functions. This paper presents a model algorithm control method using neural net-works for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one pro-duces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to il ustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

  13. Coupling of online control and inhibitory systems in children with atypical motor development: A growth curve modelling study.

    Science.gov (United States)

    Ruddock, Scott; Caeyenberghs, Karen; Piek, Jan; Sugden, David; Hyde, Christian; Morris, Sue; Rigoli, Daniela; Steenbergen, Bert; Wilson, Peter

    2016-11-01

    Previous research indicates that children with Developmental Coordination Disorder (DCD) show deficits performing online corrections, an issue exacerbated by adding inhibitory constraints; however, cross-sectional data suggests that these deficits may reduce with age. Using a longitudinal design, the aim of the study presented here was to model the coupling that occurs between inhibitory systems and (predictive) online control in typically developing children (TDC) and in those with Developmental Coordination Disorder (DCD) over an extended period of time, using a framework of interactive specialization. We predicted that TDC would show a non-linear growth pattern, consistent with re-organisation in the coupling during the middle childhood period, while DCD would display a developmental lag. A group of 196 children (111 girls and 85 boys) aged between 6 and 12years participated in the study. Children were classified as DCD according to research criteria. Using a cohort sequential design, both TDC and DCD groups were divided into age cohorts. Predictive (online) control was defined operationally by performance on a Double-Jump Reaching Task (DJRT), which was assessed at 6-month intervals over two years (5 time points in total). Inhibitory control was examined using an anti-jump condition of the DJRT paradigm whereby children were instructed to touch a target location in the hemispace opposite a cued location. For the TDC group, model comparison using growth curve analysis revealed that a quadratic trend was the most appropriate fit with evidence of rapid improvement in anti-reach performance up until middle childhood (around 8-9years of age), followed by a more gradual rate of improvement into late childhood and early adolescence. This pattern was evident on both chronometric and kinematic measures. In contrast, for children with DCD, a linear function provided the best to fit on the key metrics, with a slower rate of improvement than controls. We conclude that

  14. A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

    Directory of Open Access Journals (Sweden)

    Metin Demirtas

    2011-07-01

    Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

  15. Statistical physics of neural systems with non-additive dendritic coupling

    CERN Document Server

    Breuer, David; Memmesheimer, Raoul-Martin

    2015-01-01

    How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such non-additive dendritic processing on single neuron responses and the performance of associative memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain net...

  16. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  17. 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.

  18. Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems.

    Directory of Open Access Journals (Sweden)

    Marcus Kaiser

    2006-07-01

    Full Text Available It has been suggested that neural systems across several scales of organization show optimal component placement, in which any spatial rearrangement of the components would lead to an increase of total wiring. Using extensive connectivity datasets for diverse neural networks combined with spatial coordinates for network nodes, we applied an optimization algorithm to the network layouts, in order to search for wire-saving component rearrangements. We found that optimized component rearrangements could substantially reduce total wiring length in all tested neural networks. Specifically, total wiring among 95 primate (Macaque cortical areas could be decreased by 32%, and wiring of neuronal networks in the nematode Caenorhabditis elegans could be reduced by 48% on the global level, and by 49% for neurons within frontal ganglia. Wiring length reductions were possible due to the existence of long-distance projections in neural networks. We explored the role of these projections by comparing the original networks with minimally rewired networks of the same size, which possessed only the shortest possible connections. In the minimally rewired networks, the number of processing steps along the shortest paths between components was significantly increased compared to the original networks. Additional benchmark comparisons also indicated that neural networks are more similar to network layouts that minimize the length of processing paths, rather than wiring length. These findings suggest that neural systems are not exclusively optimized for minimal global wiring, but for a variety of factors including the minimization of processing steps.

  19. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  20. A modular neural network scheme applied to fault diagnosis in electric power systems.

    Science.gov (United States)

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  1. Neural network controller development for a magnetically suspended flywheel energy storage system

    Science.gov (United States)

    Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.

    1994-01-01

    A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

  2. Nonlinear Time-Varying Systems Identification Using Basis Sequence Expansions Combined with Neural Networks

    Institute of Scientific and Technical Information of China (English)

    顾成奎; 王正欧; 孙雅明

    2003-01-01

    A new method for identifying nonlinear time-varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non-linearity of the system, characterize time-varying dynamics of the system by the time-varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black-box modeling ability of neural networks, the presented method can identify nonlinear time-varying systems with unknown structure. In order to improve the real-time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.

  3. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    Institute of Scientific and Technical Information of China (English)

    WEI Du-Qu; LUO Xiao-Shu; ZOU Yan-Li

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N.Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs.It is found that for a given appropriate coupling strength,there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced.On the other hand,for a given intermediate system size level,there ex/sts an optimal value of coupling strength such that the intensity of firing activity reaches its maximum.These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network.

  4. Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

    Science.gov (United States)

    Leong, Harrison Monfook

    1988-01-01

    General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

  5. A gradual neural-network approach for frequency assignment in satellite communication systems.

    Science.gov (United States)

    Funabiki, N; Nishikawa, S

    1997-01-01

    A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.

  6. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    Science.gov (United States)

    Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

    2011-01-01

    This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

  7. Fuzzy System for Prognosis of Tank Failure Based on Neural Network

    Institute of Scientific and Technical Information of China (English)

    Li Guan

    2005-01-01

    A system for prognosis of tank failures was set up based on the results of analysis on fault phenomena. An algorithm incorporating fuzzy mathematics with the BP neural network was used to solve this prognosis model, and the availability of this model was also analyzed. This neural network-based fuzzy system for prognosis of tank failures has been put into operation at Huangdao oil terminal. The application results have shown that this system is effective for real-time prognosis of various potential tank failures and timely adoption of mitigative measures to avoid major tank accidents, which would have great significance for safeguarding the safe operation of the oil terminal.

  8. Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique.

    Science.gov (United States)

    Yu, Wen

    2015-07-01

    Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown nonlinear systems, this neural sliding mode control can passify and stabilize them. This passivity method is validated with a simulation and real experiment tests.

  9. Zero phase error control based on neural compensation for flight simulator servo system

    Institute of Scientific and Technical Information of China (English)

    Liu Jinkun; He Peng; Er Lianjie

    2006-01-01

    Using the future desired input value, zero phase error controller enables the overall system's frequency response exhibit zero phase shift for all frequencies and a small gain error at low frequency range, and based on this, a new algorithm is presented to design the feedforward controller. However, zero phase error controller is only suitable for certain linear system. To reduce the tracking error and improve robustness, the design of the proposed feedforward controller uses a neural compensation based on diagonal recurrent neural network. Simulation and real-time control results for flight simulator servo system show the effectiveness of the proposed approach.

  10. 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....

  11. Somatostatin-expressing inhibitory interneurons in cortical circuits

    Directory of Open Access Journals (Sweden)

    Iryna Yavorska

    2016-09-01

    Full Text Available Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing properties, and expression of molecular markers. Several of these classes of SOM neurons show unique dynamics and characteristics, such as facilitating synapses, specific axonal projections, intralaminar input, and top-down modulation, which suggest possible computational roles. SOM cells can be differentially modulated by behavioral state depending on their class, sensory system, and behavioral paradigm. The functional effects of such modulation have been studied with optogenetic manipulation of SOM cells, which produces effects on learning and memory, task performance, and the integration of cortical activity. Different classes of SOM cells participate in distinct disinhibitory circuits with different inhibitory partners and in different cortical layers. Through these disinhibitory circuits, SOM cells help encode the behavioral relevance of sensory stimuli by regulating the activity of cortical neurons based on subcortical and intracortical modulatory input. Associative learning leads to long-term changes in the strength of connectivity of SOM cells with other neurons, often influencing the strength of inhibitory input they receive. Thus despite their heterogeneity and variability across cortical areas, current evidence shows that SOM neurons perform unique neural computations, forming not only distinct molecular but also functional subclasses of cortical inhibitory interneurons.

  12. A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems

    Directory of Open Access Journals (Sweden)

    Guojiang Xiong

    2013-01-01

    Full Text Available Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.

  13. Coding of level of ambiguity within neural systems mediating choice

    Science.gov (United States)

    Lopez-Paniagua, Dan; Seger, Carol A.

    2013-01-01

    Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common “fronto—parietal—striatal” network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum). PMID:24367286

  14. An optimization spiking neural p system for approximately solving combinatorial optimization problems.

    Science.gov (United States)

    Zhang, Gexiang; Rong, Haina; Neri, Ferrante; Pérez-Jiménez, Mario J

    2014-08-01

    Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

  15. The simulation and interpretation of free turbulence with a cognitive neural system

    Science.gov (United States)

    Giralt, Francesc; Arenas, A.; Ferre-Giné, J.; Rallo, R.; Kopp, G. A.

    2000-07-01

    An artificial neural network, based on fuzzy ARTMAP, that is capable of learning the basic nonlinear dynamics of a turbulent velocity field is presented. The neural system is capable of generating a detailed multipoint time record with the same structural characteristics and basic statistics as those of the original instantaneous velocity field used for training. The good performance of the proposed architecture is demonstrated by the generation of synthetic two-dimensional velocity data at eight different positions along the homogeneous (spanwise) direction in the far region (x/D=420) of a turbulent wake flow generated behind a cylinder at Re=1 200. The analysis of the synthetic velocity field, carried out with spectral techniques, POD and pattern recognition, reveals that the proposed neural system is capable of capturing the highly nonlinear dynamics of free turbulence and of reproducing the sequence of individual classes of relevant events present in turbulent wake flows. The trained neural system also yields patterns of the coherent structures embedded in the flow when presented with input data containing partial information of the instantaneous velocity maps of these events. In this way, the neural network is used as an expert system that helps in the structural interpretation of turbulence in a wake flow.

  16. Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

    Science.gov (United States)

    Shepanski, J. F.; Macy, S. A.

    1987-01-01

    A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

  17. The role of GABAergic system on the inhibitory effect of ghrelin on food intake in neonatal chicks.

    Science.gov (United States)

    Jonaidi, H; Abbassi, L; Yaghoobi, M M; Kaiya, H; Denbow, D M; Kamali, Y; Shojaei, B

    2012-06-27

    Ghrelin is a gut-brain peptide that has a stimulatory effect on food intake in mammals. In contrast, this peptide decreases food intake in neonatal chicks when injected intracerebroventricularly (ICV). In mammals, neuropeptide Y (NPY) mediates the orexigenic effect of ghrelin whereas in chicks it appears that corticotrophin releasing factor (CRF) is partially involved in the inhibitory effect of ghrelin on food intake. Gamma aminobutyric acid (GABA) has a stimulatory effect on food intake in mammals and birds. In this study we investigated whether the anorectic effect of ghrelin is mediated by the GABAergic system. In Experiment 1, 3h-fasted chicks were given an ICV injection of chicken ghrelin and picrotoxin, a GABA(A) receptors antagonist. Picrotoxin decreased food intake compared to the control chicks indicating a stimulatory effect of GABA(A) receptors on food intake. However, picrotoxin did not alter the inhibitory effect of ghrelin on food intake. In Experiment 2, THIP hydrochloride, a GABA(A) receptor agonist, was used in place of picrotoxin. THIP hydrochloride appeared to partially attenuate the decrease in food intake induced by ghrelin at 30 min postinjection. In Experiment 3, the effect of ICV injection of chicken ghrelin on gene expression of glutamate decarboxylase (GAD)(1) and GAD(2), GABA synthesis enzymes in the brain stem including hypothalamus, was investigated. The ICV injection of chicken ghrelin significantly reduced GAD(2) gene expression. These findings suggest that ghrelin may decrease food intake in neonatal chicks by reducing GABA synthesis and thereby GABA release within brain feeding centers.

  18. Distributed neural system for emotional intelligence revealed by lesion mapping.

    Science.gov (United States)

    Barbey, Aron K; Colom, Roberto; Grafman, Jordan

    2014-03-01

    Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

  19. Application of neural networks with orthogonal activation functions in control of dynamical systems

    Science.gov (United States)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  20. Adaptive capability of recurrent neural networks with fixed weights for series-parallel system identification.

    Science.gov (United States)

    Lo, James Ting-Ho

    2009-11-01

    By a fundamental neural filtering theorem, a recurrent neural network with fixed weights is known to be capable of adapting to an uncertain environment. This letter reports some mathematical results on the performance of such adaptation for series-parallel identification of a dynamical system as compared with the performance of the best series-parallel identifier possible under the assumption that the precise value of the uncertain environmental process is given. In short, if an uncertain environmental process is observable (not necessarily constant) from the output of a dynamical system or constant (not necessarily observable), then a recurrent neural network exists as a series-parallel identifier of the dynamical system whose output approaches the output of an optimal series-parallel identifier using the environmental process as an additional input.

  1. Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

    CERN Document Server

    Melin, Patricia

    2012-01-01

    This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

  2. Neural network system for purposeful behavior based on foveal visual preprocessor

    Science.gov (United States)

    Golovan, Alexander V.; Shevtsova, Natalia A.; Klepatch, Arkadi A.

    1996-10-01

    Biologically plausible model of the system with an adaptive behavior in a priori environment and resistant to impairment has been developed. The system consists of input, learning, and output subsystems. The first subsystems classifies input patterns presented as n-dimensional vectors in accordance with some associative rule. The second one being a neural network determines adaptive responses of the system to input patterns. Arranged neural groups coding possible input patterns and appropriate output responses are formed during learning by means of negative reinforcement. Output subsystem maps a neural network activity into the system behavior in the environment. The system developed has been studied by computer simulation imitating a collision-free motion of a mobile robot. After some learning period the system 'moves' along a road without collisions. It is shown that in spite of impairment of some neural network elements the system functions reliably after relearning. Foveal visual preprocessor model developed earlier has been tested to form a kind of visual input to the system.

  3. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  4. Neural Signatures of Number Processing in Human Infants: Evidence for Two Core Systems Underlying Numerical Cognition

    Science.gov (United States)

    Hyde, Daniel C.; Spelke, Elizabeth S.

    2011-01-01

    Behavioral research suggests that two cognitive systems are at the foundations of numerical thinking: one for representing 1-3 objects in parallel and one for representing and comparing large, approximate numerical magnitudes. We tested for dissociable neural signatures of these systems in preverbal infants by recording event-related potentials…

  5. Identification of protein kinase C inhibitory activity associated with a polypeptide isolated from a phage display system with homology to PCM-1, the pericentriolar material-1 protein.

    Science.gov (United States)

    Chakravarthy, Balu; Ménard, Michel; Brown, Leslie; Atkinson, Trevor; Whitfield, James

    2012-07-20

    We had previously identified a protein kinase C (PKC) inhibitor in murine neuroblastoma cells (Chakravarthy et al. [1]). Similar PKC inhibitory activity was also found in adult rat brain. Using polyclonal antibodies raised against the partially purified PKC inhibitor from rat brain as bait, we isolated a putative brain PKC inhibitor using a T-7 phage display system expressing human brain cDNA library. After enriching the phage population expressing the putative PKC inhibitor with four rounds of biopanning using ELISA and in vitro PKC binding assays, we identified a phage clone that expressed a product with significant PKC inhibitory activity. We have cloned and expressed this cDNA in a bacterial system and purified the recombinant protein. This polypeptide (174 amino acids) is highly homologous to a region of the 228-kDa PCM-1, the human pericentriolar material 1 protein. We have mapped this polypeptide's PKC-inhibitory domain and shown its PKC inhibitory activity in vitro. However, it will need to be determined whether the full-length PCM-1 protein possesses PKC inhibitory activity in vivo, and if so, how this might contribute to PCM-1's recently demonstrated roles in ciliogenesis and neurogenesis.

  6. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

    Science.gov (United States)

    Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

    2017-01-01

    To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

  7. Efficient decoding with steady-state Kalman filter in neural interface systems.

    Science.gov (United States)

    Malik, Wasim Q; Truccolo, Wilson; Brown, Emery N; Hochberg, Leigh R

    2011-02-01

    The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5±0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

  8. Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

    Indian Academy of Sciences (India)

    Abdurahman Kadir; Xing-Yuan Wang; Yu-Zhang Zhao

    2011-06-01

    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 online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

  9. Corrosion by bacteria of concrete in sewerage systems and inhibitory effects of formates on their growth.

    Science.gov (United States)

    Yamanaka, Tateo; Aso, Iwao; Togashi, Shunsuke; Tanigawa, Minoru; Shoji, Kazuo; Watanabe, Tsugumichi; Watanabe, Naoki; Maki, Kazuo; Suzuki, Hiroshi

    2002-05-01

    Not only sulfur-oxidizing bacteria but also an acidophilic iron-oxidizing bacterium (or bacteria) were found in the corroded concrete from several sewerage systems in Japan. The surface pH of concrete test piece exposed to an atmosphere containing hydrogen sulfide of the concentrations more than 600 ppm in the systems was usually below 2 after a month. This was attributable to ability of the sulfur-oxidizing bacteria to grow in the thin water layer which contained hydrogen sulfide and covered the piece even when the surface pH of concrete was 12-13. When the sulfuroxidizing bacteria grew in the surface of concrete and produced sulfuric acid, the pH of the inner parts of concrete was lowered where the bacteria were hardly found. Probably, sulfuric acid formed by the bacteria in the surface parts penetrated into the inner parts. The different species of sulfur-oxidizing bacteria were found in different sewerage systems. The growth of the sulfur-oxidizing and acidophilic iron-oxidizing bacteria was completely inhibited by formates, especially by calcium formate of concentrations more than 50 mM. Calcium formate can protect concrete in sewerage systems from bacterial corrosion.

  10. 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.

  11. Adaptive neural control for a class of nonlinearly parametric time-delay systems.

    Science.gov (United States)

    Ho, Daniel W C; Li, Junmin; Niu, Yugang

    2005-05-01

    In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.

  12. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  13. Different Avalanche Behaviors in Different Specific Areas of a System Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAOXiao-Wei; CHENTian-Lun

    2003-01-01

    Based on the standard self-organizing map (SOM) neural network model and an integrate-and-fire mecha-nism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We find power-law distribution behavior of avalanche size in our model. But more importantly, we find there are different avalanche distribution behaviors in different specific areas of our system, which are formed by the topological learning process of the SOM net.

  14. Neural Networks for Self-tuning Control Systems

    Directory of Open Access Journals (Sweden)

    A. Noriega Ponce

    2004-01-01

    Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

  15. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  16. Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form.

    Science.gov (United States)

    Peng, Zhouhua; Wang, Dan; Wang, Jun

    2016-06-22

    This paper presents a predictor-based neural dynamic surface control (PNDSC) design method for a class of uncertain nonlinear systems in a strict-feedback form. In contrast to existing NDSC approaches where the tracking errors are commonly used to update neural network weights, a predictor is proposed for every subsystem, and the prediction errors are employed to update the neural adaptation laws. The proposed scheme enables smooth and fast identification of system dynamics without incurring high-frequency oscillations, which are unavoidable using classical NDSC methods. Furthermore, the result is extended to the PNDSC with observer feedback, and its robustness against measurement noise is analyzed. Numerical and experimental results are given to demonstrate the efficacy of the proposed PNDSC architecture.

  17. Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

    Institute of Scientific and Technical Information of China (English)

    LI Chun-hua; ZHU Xin-jian; SUI Sheng; HU Wan-qi

    2009-01-01

    In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

  18. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

    Institute of Scientific and Technical Information of China (English)

    Chun-hua LI; Xin-jian ZHU; Guang-yi CAO; Wan-qi HU; Sheng SUI; Ming-ruo HU

    2009-01-01

    To extract the maximum power from a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller (FNNC), which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the fuzzy logic control algorithm.

  19. RBF neural network based $\\mathcal{H}_{\\infty}$ synchronization for unknown chaotic systems

    Indian Academy of Sciences (India)

    Choon Ki Ahn

    2010-08-01

    In this paper, we propose a new $\\mathcal{H}_{\\infty}$ synchronization strategy, called a Radial Basis Function Neural Network $\\mathcal{H}_{\\infty}$ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the proposed framework, a radial basis function neural network (RBFNN) is constructed as an alternative to approximate the unknown nonlinear function of the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an $\\mathcal{H}_{\\infty}$ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed RBFNNHS scheme.

  20. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  1. SINR Prediction in Mobile CDMA Systems by Linear and Nonlinear Artificial Neural-Network-Based Predictors

    Directory of Open Access Journals (Sweden)

    Nahid Ardalani

    2011-07-01

    Full Text Available This article describes linear and nonlinear Artificial Neural Network(ANN-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX process for Signal to Interference plus Noise Ratio (SINR prediction in Direct Sequence Code Division Multiple Access (DS/CDMA systems. The Multi Layer Perceptron (MLP neural network with nonlinear function is used as nonlinear neural network and Adaptive Linear (Adaline predictor is used as linear predictor. The problem of complexity of the MLP and Adaline structures is solved by using the Minimum Mean Squared Error (MMSE principle to select the optimal numbers of input and hidden nodes by try and error role. Simulation results show that both of MLP and Adaline optimal neural networks can track the effect of deep fading due to using a 1.8 GHZ carrier frequency at the urban mobile speeds of 10 km/h, 50 km/h and 120 km/h with tolerable estimation errors. Therefore, the neural networkbased predictor is well suitable SINR-based predictor in closedloop power control to combat multi path fading in CDMA systems.

  2. Neural mirroring and social interaction: Motor system involvement during action observation relates to early peer cooperation.

    Science.gov (United States)

    Endedijk, H M; Meyer, M; Bekkering, H; Cillessen, A H N; Hunnius, S

    2017-04-01

    Whether we hand over objects to someone, play a team sport, or make music together, social interaction often involves interpersonal action coordination, both during instances of cooperation and entrainment. Neural mirroring is thought to play a crucial role in processing other's actions and is therefore considered important for social interaction. Still, to date, it is unknown whether interindividual differences in neural mirroring play a role in interpersonal coordination during different instances of social interaction. A relation between neural mirroring and interpersonal coordination has particularly relevant implications for early childhood, since successful early interaction with peers is predictive of a more favorable social development. We examined the relation between neural mirroring and children's interpersonal coordination during peer interaction using EEG and longitudinal behavioral data. Results showed that 4-year-old children with higher levels of motor system involvement during action observation (as indicated by lower beta-power) were more successful in early peer cooperation. This is the first evidence for a relation between motor system involvement during action observation and interpersonal coordination during other instances of social interaction. The findings suggest that interindividual differences in neural mirroring are related to interpersonal coordination and thus successful social interaction. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. Adaptive Neural Control of MIMO Nonstrict-Feedback Nonlinear Systems With Time Delay.

    Science.gov (United States)

    Zhao, Xudong; Yang, Haijiao; Karimi, Hamid Reza; Zhu, Yanzheng

    2016-06-01

    In this paper, an adaptive neural output-feedback tracking controller is designed for a class of multiple-input and multiple-output nonstrict-feedback nonlinear systems with time delay. The system coefficient and uncertain functions of our considered systems are both unknown. By employing neural networks to approximate the unknown function entries, and constructing a new input-driven filter, a backstepping design method of tracking controller is developed for the systems under consideration. The proposed controller can guarantee that all the signals in the closed-loop systems are ultimately bounded, and the time-varying target signal can be tracked within a small error as well. The main contributions of this paper lie in that the systems under consideration are more general, and an effective design procedure of output-feedback controller is developed for the considered systems, which is more applicable in practice. Simulation results demonstrate the efficiency of the proposed algorithm.

  4. A multiple circular path convolution neural network system for detection of mammographic masses.

    Science.gov (United States)

    Lo, Shih-Chung B; Li, Huai; Wang, Yue; Kinnard, Lisa; Freedman, Matthew T

    2002-02-01

    A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.

  5. 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.

  6. Inhibitory Action of Ethanolic Extract of Seeds of Moringa oleifera Lam. On Systemic and Local Anaphylaxis.

    Science.gov (United States)

    Mahajan, Shailaja G; Mehta, Anita A

    2007-10-01

    The current study characterizes the mechanism by which the seed extract of Moringa oleifera Lam (Moringaceae) decreases the mast cell-mediated immediate type hypersensitivity reaction. The immediate type hypersensitivity reaction is involved in many allergic diseases such as asthma and allergic rhinitis. Moringa oleifera, a shrub widely used in the traditional medicine in India, has been reported to possess anti-cancer, hypotensive, anti-arthritic, and anti-inflammatory activities. In the present study, the effects of the ethanolic extract of seeds of Moringa oleifera (MOEE-herbal remedy) on systemic and local anaphylaxis were investigated. The potential anti-anaphylactic effect of MOEE was studied in a mouse model of Compound 48/80-induced systemic anaphylactic shock. Passive cutaneous anaphylaxis activated by anti IgE-antibody was also used to assess the effect of MOEE. In addition, rat peritoneal mast cells (RPMC) were used to investigate the effect of MOEE on histamine release induced by compound 48/80. When administered 1 hr before 48/80 injection, MOEE at doses of 0.001-1.000 g/kg completely inhibited the inducible induced anaphylactic shock. MOEE significantly inhibited passive cutaneous anaphylaxis activated by anti-IgE antibody at a dose of 1 g/kg. When MOEE extract was given as pretreatment at concentrations ranging 0.1-100 mg/ml, the histamine release from the mast cells that was induced by the 48/80 was reduced in a dose-dependent manner. These results suggest a potential role for MOEE as a source of anti-anaphylactic agents for use in allergic disorders.

  7. A VLSI Neural Monitoring System With Ultra-Wideband Telemetry for Awake Behaving Subjects.

    Science.gov (United States)

    Greenwald, E; Mollazadeh, M; Hu, C; Wei Tang; Culurciello, E; Thakor, V

    2011-04-01

    Long-term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for neuroscience and neuroengineering applications. Here, we present a miniature, lightweight, and low-power recording system for monitoring neural activity in awake behaving animals. The system integrates two custom designed very-large-scale integrated chips, a neural interface module fabricated in 0.5 μm complementary metal-oxide semiconductor technology and an ultra-wideband transmitter module fabricated in a 0.5 μm silicon-on-sapphire (SOS) technology. The system amplifies, filters, digitizes, and transmits 16 channels of neural data at a rate of 1 Mb/s. The entire system, which includes the VLSI circuits, a digital interface board, a battery, and a custom housing, is small and lightweight (24 g) and, thus, can be chronically mounted on small animals. The system consumes 4.8 mA and records continuously for up to 40 h powered by a 3.7-V, 200-mAh rechargeable lithium-ion battery. Experimental benchtop characterizations as well as in vivo multichannel neural recordings from awake behaving rats are presented here.

  8. System identification: a multi-signal approach for probing neural cardiovascular regulation.

    Science.gov (United States)

    Xiao, Xinshu; Mullen, Thomas J; Mukkamala, Ramakrishna

    2005-06-01

    Short-term, beat-to-beat cardiovascular variability reflects the dynamic interplay between ongoing perturbations to the circulation and the compensatory response of neurally mediated regulatory mechanisms. This physiologic information may be deciphered from the subtle, beat-to-beat variations by using digital signal processing techniques. While single signal analysis techniques (e.g., power spectral analysis) may be employed to quantify the variability itself, the multi-signal approach of system identification permits the dynamic characterization of the neural regulatory mechanisms responsible for coupling the variability between signals. In this review, we provide an overview of applications of system identification to beat-to-beat variability for the quantitative characterization of cardiovascular regulatory mechanisms. After briefly summarizing the history of the field and basic principles, we take a didactic approach to describe the practice of system identification in the context of probing neural cardiovascular regulation. We then review studies in the literature over the past two decades that have applied system identification for characterizing the dynamical properties of the sinoatrial node, respiratory sinus arrhythmia, and the baroreflex control of sympathetic nerve activity, heart rate and total peripheral resistance. Based on this literature review, we conclude by advocating specific methods of practice and that future research should focus on nonlinear and time-varying behaviors, validation of identification methods, and less understood neural regulatory mechanisms. Ultimately, we hope that this review stimulates such future investigations by both new and experienced system identification researchers.

  9. Real-time camera-based face detection using a modified LAMSTAR neural network system

    Science.gov (United States)

    Girado, Javier I.; Sandin, Daniel J.; DeFanti, Thomas A.; Wolf, Laura K.

    2003-03-01

    This paper describes a cost-effective, real-time (640x480 at 30Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3D head position and orientation tracking system. The work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. The proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. The sub-window is segmented, and each part is fed to a neural network layer consisting of a Kohonen Self-Organizing Map (SOM). The output of the SOM neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. The system is also rotationally and size invariant to a certain degree.

  10. Neural network based automatic limit prediction and avoidance system and method

    Science.gov (United States)

    Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

    2001-01-01

    A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

  11. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  12. Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.H. [Kangwon National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Park, J.K. [Seoul National Univ. (Korea, Republic of). Dept. of Electrical Engineering; Hwang, K.J. [Univ. of Ulsan (Korea, Republic of). Dept. of Electrical Engineering; Kim, S.H. [Korea Electric Power Co., Seoul (Korea, Republic of). Power System Control Dept.

    1995-08-01

    In this paper, a hybrid model for short-term load forecast that integrates artificial neural networks and fuzzy expert systems is presented. The forecasted load is obtained by passing through two steps. In the first procedure, the artificial neural networks are trained with the load patterns corresponding to the forecasting hour, and the provisional forecasted load is obtained by the trained artificial neural networks. In the second procedure, the fuzzy expert systems modify the provisional forecasted load considering the possibility of load variation due to changes in temperature and the load behavior of holiday. In the test case of 1994 for implementation in short term load forecasting expert system of Korea Electric Power Corporation (KEPCO), the proposed hybrid model provided good forecasting accuracy of the mean absolute percentage errors below 1.3%. The comparison results with exponential smoothing method showed the efficiency and accuracy of the hybrid model.

  13. Coupling of online control and inhibitory systems in children with atypical motor development: A growth curve modelling study

    NARCIS (Netherlands)

    Ruddock, S.; Caeyenberghs, K.; Piek, J.P.; Sugden, D.A.; Hyde, C.; Morris, S.L.; Rigoli, D.; Steenbergen, B.; Wilson, P.H.

    2016-01-01

    Introduction: Previous research indicates that children with Developmental Coordination Disorder (DCD) show deficits performing online corrections, an issue exacerbated by adding inhibitory constraints; however, cross-sectional data suggests that these deficits may reduce with age. Using a longitudi

  14. Chaos Control and Anti-control via a Fuzzy Neural Network Inverse System Method

    Institute of Scientific and Technical Information of China (English)

    任海鹏; 刘丁

    2002-01-01

    We propose a new method for chaos control and anti-control, which is referred to as the fuzzy-neural network inverse system method (FNNIS). The Sugeno-type fuzzy-neural network (FNN) is employed to learn the kinetics of the system to be controlled. Then the FNN model is used with the inverse system method to make the system to be controlled to track the reference input. If the system to be controlled is chaotic and the reference input is non-chaotic, chaos control can be implemented via the FNNIS method. If the system to be controlled is nonchaotic and the reference input is chaotic, chaos anti-control can be implemented. Theorems about the effect of the FNN model error upon control are established. The simulation results show that this method is feasible and effective for chaos control and anti-control.

  15. Using a hybrid neural/expert system for data base mining in market survey data

    Energy Technology Data Exchange (ETDEWEB)

    Ciesielski, V.; Palstra, G. [Royal Melbourne Inst. of Technology (Australia)

    1996-12-31

    This paper describes the application of a hybrid neural/expert system network to the task of finding significant events in a market research data base. Neural networks trained by backward error propagation are used to classify trends in the time series data. A rule system then uses these classifications, knowledge of market research analysis techniques and external events which influence the time series, to infer the significance of the data. The system achieved 86% recall and 100% precision on a test set of 6 months of survey data. This was significantly better than could be achieved by a system using linear regression together with a rule system. Both systems were able to perform analysis of the test data in under 5 minutes. The manual analysis of the same data took a human expert over four working days.

  16. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  17. Dissociated emergent-response system and fine-processing system in human neural network and a heuristic neural architecture for autonomous humanoid robots.

    Science.gov (United States)

    Yan, Xiaodan

    2010-01-01

    The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC) and the anterior cingulate cortex (ACC) were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

  18. Dissociated Emergent-Response System and Fine-Processing System in Human Neural Network and a Heuristic Neural Architecture for Autonomous Humanoid Robots

    Directory of Open Access Journals (Sweden)

    Xiaodan Yan

    2010-01-01

    Full Text Available The current study investigated the functional connectivity of the primary sensory system with resting state fMRI and applied such knowledge into the design of the neural architecture of autonomous humanoid robots. Correlation and Granger causality analyses were utilized to reveal the functional connectivity patterns. Dissociation was within the primary sensory system, in that the olfactory cortex and the somatosensory cortex were strongly connected to the amygdala whereas the visual cortex and the auditory cortex were strongly connected with the frontal cortex. The posterior cingulate cortex (PCC and the anterior cingulate cortex (ACC were found to maintain constant communication with the primary sensory system, the frontal cortex, and the amygdala. Such neural architecture inspired the design of dissociated emergent-response system and fine-processing system in autonomous humanoid robots, with separate processing units and another consolidation center to coordinate the two systems. Such design can help autonomous robots to detect and respond quickly to danger, so as to maintain their sustainability and independence.

  19. Integrating multiple sensory systems to modulate neural networks controlling posture.

    Science.gov (United States)

    Lavrov, I; Gerasimenko, Y; Burdick, J; Zhong, H; Roy, R R; Edgerton, V R

    2015-12-01

    In this study we investigated the ability of sensory input to produce tonic responses in hindlimb muscles to facilitate standing in adult spinal rats and tested two hypotheses: 1) whether the spinal neural networks below a complete spinal cord transection can produce tonic reactions by activating different sensory inputs and 2) whether facilitation of tonic and rhythmic responses via activation of afferents and with spinal cord stimulation could engage similar neuronal mechanisms. We used a dynamically controlled platform to generate vibration during weight bearing, epidural stimulation (at spinal cord level S1), and/or tail pinching to determine the postural control responses that can be generated by the lumbosacral spinal cord. We observed that a combination of platform displacement, epidural stimulation, and tail pinching produces a cumulative effect that progressively enhances tonic responses in the hindlimbs. Tonic responses produced by epidural stimulation alone during standing were represented mainly by monosynaptic responses, whereas the combination of epidural stimulation and tail pinching during standing or epidural stimulation during stepping on a treadmill facilitated bilaterally both monosynaptic and polysynaptic responses. The results demonstrate that tonic muscle activity after complete spinal cord injury can be facilitated by activation of specific combinations of afferent inputs associated with load-bearing proprioception and cutaneous input in the presence of epidural stimulation and indicate that whether activation of tonic or rhythmic responses is generated depends on the specific combinations of sources and types of afferents activated in the hindlimb muscles.

  20. Two separate, but interacting, neural systems for familiarity and novelty detection: a dual-route mechanism.

    Science.gov (United States)

    Kafkas, Alexandros; Montaldi, Daniela

    2014-05-01

    It has long been assumed that familiarity- and novelty-related processes fall on a single continuum drawing on the same cognitive and neural mechanisms. The possibility that familiarity and novelty processing involve distinct neural networks was explored in a functional magnetic resonance imaging study (fMRI), in which familiarity and novelty judgments were made in contexts emphasizing either familiarity or novelty decisions. Parametrically modulated BOLD responses to familiarity and novelty strength were isolated in two separate, nonoverlapping brain networks. The novelty system involved brain regions along the ventral visual stream, the hippocampus, and the perirhinal and parahippocampal cortices. The familiarity system, on the other hand, involved the dorsomedial thalamic nucleus, and regions within the medial prefrontal cortex and the medial and lateral parietal cortex. Convergence of the two networks, treating familiarity and novelty as a single continuum was only found in a fronto-parietal network. Finally, the orbitomedial prefrontal cortex was found to be sensitive to reported strength/confidence, irrespective of stimulus' familiarity or novelty. This pattern of results suggests a dual-route mechanism supported by the existence of two distinct but interacting functional systems for familiarity and novelty. Overall, these findings challenge current assumptions regarding the neural systems that support the processing of novel and familiar information, and have important implications for research into the neural bases of recognition memory.

  1. Comparable mechanisms for action and language: Neural systems behind intentions, goals and means

    NARCIS (Netherlands)

    Schie, H.T. van; Toni, I.; Bekkering, H.

    2006-01-01

    In this position paper we explore correspondence between neural systems for language and action starting from recent electrophysiological findings on the roles of posterior and frontal areas in goal-directed grasping actions. The paper compares the perceptual and motor organization for action and

  2. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  3. Stress, Neural Systems, and Genetic Code: An Interview with Neuroscientist Judy Cameron. Perspectives

    Science.gov (United States)

    National Scientific Council on the Developing Child, 2006

    2006-01-01

    Research indicates some early life stresses can have a profound impact, resulting in changes in brain function and behavior, and even differences in the ways some genes express their particular genetic code signature. At various times during early development, different neural systems appear to have an increased sensitivity to stress and can…

  4. Stress, Neural Systems, and Genetic Code: An Interview with Neuroscientist Judy Cameron. Perspectives

    Science.gov (United States)

    National Scientific Council on the Developing Child, 2006

    2006-01-01

    Research indicates some early life stresses can have a profound impact, resulting in changes in brain function and behavior, and even differences in the ways some genes express their particular genetic code signature. At various times during early development, different neural systems appear to have an increased sensitivity to stress and can…

  5. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...

  6. A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

    Science.gov (United States)

    Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

    2016-09-01

    Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

  7. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  8. Genetic Algorithms, Neural Networks, and Time Effectiveness Algorithm Based Air Combat Intelligence Simulation System

    Institute of Scientific and Technical Information of China (English)

    曾宪钊; 成冀; 安欣; 方礼明

    2002-01-01

    This paper introduces a new Air Combat Intelligence Simulation System (ACISS) in a 32 versus 32 air combat, describes three methods: Genetic Algorithms (GA) in the multi-targeting decision and Evading Missile Rule Base learning, Neural Networks (NN) in the maneuvering decision, and Time Effectiveness Algorithm (TEA) in the adjudicating an air combat and the evaluating evading missile effectiveness.

  9. QUADRATIC PROGRAMMING NEURAL NETWORK BASED INTEGRATED SPACE-TIME INTERFERENCE SUPPRESSION IN CDMA SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    Song Rongfang; Bi Guangguo

    2001-01-01

    Quadratic programming models for integrated space-time interference suppression in CDMA systems are proposed in this paper. The models integrate the advantages of smart antenna and RAKE receiver, mitigate multiuser access interference (MAI) and interchip interference (ICI),and combine multipath components. The zero-forcing conditions are derived. Neural network implementation of the models is also studied.

  10. A System for Predicting Subcellular Localization of Yeast Genome Using Neural Network

    CERN Document Server

    Thampi, Sabu M

    2007-01-01

    The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. Many efforts have been made to predict protein subcellular localization. This paper aims to merge the artificial neural networks and bioinformatics to predict the location of protein in yeast genome. We introduce a new subcellular prediction method based on a backpropagation neural network. The results show that the prediction within an error limit of 5 to 10 percentage can be achieved with the system.

  11. Identification Simulation for Dynamical System Based on Genetic Algorithm and Recurrent Multilayer Neural Network

    Institute of Scientific and Technical Information of China (English)

    鄢田云; 张翠芳; 靳蕃

    2003-01-01

    Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).

  12. Design and performance analysis of tracking controller for uncertain nonlinear composite system using neural networks

    Institute of Scientific and Technical Information of China (English)

    Endong LIU; Yuanwei JING; Siying ZHANG

    2005-01-01

    Based on high order dynamic neural network,this paper presents the tracking problem for uncertain nonlinear composite system,which contains external disturbance,whose nonlinearities are assumed to be unknown.A smooth controller is designed to guarantee a uniform ultimate boundedness property for the tracking error and all other signals in the closed loop.Certain measures are utilized to test its performance.No a priori knowledge of an upper bound on the "optimal" weight and modeling error is required;the weights of neural networks are updated on-line.Numerical simulations performed on a simple example illustrate and clarify the approach.

  13. Time Series Forecasting of Daily Reference Evapotranspiration by Neural Network Ensemble Learning for Irrigation System

    Science.gov (United States)

    Manikumari, N.; Murugappan, A.; Vinodhini, G.

    2017-07-01

    Time series forecasting has gained remarkable interest of researchers in the last few decades. Neural networks based time series forecasting have been employed in various application areas. Reference Evapotranspiration (ETO) is one of the most important components of the hydrologic cycle and its precise assessment is vital in water balance and crop yield estimation, water resources system design and management. This work aimed at achieving accurate time series forecast of ETO using a combination of neural network approaches. This work was carried out using data collected in the command area of VEERANAM Tank during the period 2004 - 2014 in India. In this work, the Neural Network (NN) models were combined by ensemble learning in order to improve the accuracy for forecasting Daily ETO (for the year 2015). Bagged Neural Network (Bagged-NN) and Boosted Neural Network (Boosted-NN) ensemble learning were employed. It has been proved that Bagged-NN and Boosted-NN ensemble models are better than individual NN models in terms of accuracy. Among the ensemble models, Boosted-NN reduces the forecasting errors compared to Bagged-NN and individual NNs. Regression co-efficient, Mean Absolute Deviation, Mean Absolute Percentage error and Root Mean Square Error also ascertain that Boosted-NN lead to improved ETO forecasting performance.

  14. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  15. Building an Early Warning System for Crude Oil Price Using Neural Network

    Directory of Open Access Journals (Sweden)

    Wonho Song

    2010-12-01

    Full Text Available In this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning sysIn this paper, a crisis index for the oil price shock is defined and a neural network model is specified for the prediction of the crisis index. This paper contributes to the literature in three ways. First, we build an early warning system for crude oil price. Although the oil price became one of the most important price index recently, no research efforts have been made to build an early warning system for crude oil price. Second, the neural network (NN model is used to construct the early warning system. Most early warning systems are built based on the signaling approach. In this paper, we show that the neural network models are more flexible and have greater potential as EWS than the signaling approach. Third, we allow the multi-level crisis index. Previous models allowed only a zero/one crisis index whereas our model permits as many levels as possible. With this new model, we try to answer whether the oil price collapse following the historical peak in 2008 was predictable. We compare the results from the NN model with those from the ordered probit (OP model, and show that the oil price crisis and the following crash were predictable by the NN model, but not by the OP model.

  16. Neural-net based unstable machine identification using individual energy functions. [Transient disturbances in power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Institut Nikola Tesla, Belgrade (Yugoslavia)); Sobajic, D.J.; Yohhan Pao (Case Western Reserve Univ., Cleveland, OH (United States))

    1991-10-01

    The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).

  17. 3D COLOR OBJECTS RECOGNITION SYSTEM USING AN ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Omar BENCHAREF

    2011-06-01

    Full Text Available Hu & Zernike moments have always been used for grey image representation. In this study we have tried to employ them directly for color image description. This would enable us to keep the maximum amount of information given by the image colors. Regarding the classification process we have opted for the neural networks classifier, which enable to implicitly detect complex nonlinear relationships between dependent and independent variables, and to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. In this document, we present a comparative study between different 3D color objects recognition systems. We have used a variety of topologies of Neural Multi-layer Networks (simple, nested and parallel networks, to come up eventually with a suggestion of a multi-Oriented Neural Networks.

  18. Simulation of oxygen saturation of hemoglobin solution, RBC suspension and hemosome by a neural network system.

    Science.gov (United States)

    Kan, P; Chen, W K; Lee, C J

    1996-03-01

    Hemoglobin-based artificial blood substitutes as oxygen carrier is advantageous over current plasma expander. In this study, oxygen saturation of hemoglobin solution, red blood cell suspension and artificial blood substitute under various conditions were measured by yeast-consuming-oxygen experiments instead of spectrophotometer. The empirical results were assigned into training feedforward back-propagation neural network system in order to simulate the oxygen saturation model modulated by those factors such as pH, [Cl-], [2,3-DPG], pO2 and pCO2. Consequently, this neural network is able to simulate accurately the oxygen saturation of Hb solution. The prediction of hemosome is not agreed well possible because of the resistance of transport of oxygen. However, the results showed neural net can offer a simple and convenient way in comparison with the conventional methods, especially in dealing with complex and ambiguous problem.

  19. Holistic neural coding of Chinese character forms in bilateral ventral visual system.

    Science.gov (United States)

    Mo, Ce; Yu, Mengxia; Seger, Carol; Mo, Lei

    2015-02-01

    How are Chinese characters recognized and represented in the brain of skilled readers? Functional MRI fast adaptation technique was used to address this question. We found that neural adaptation effects were limited to identical characters in bilateral ventral visual system while no activation reduction was observed for partially overlapping characters regardless of the spatial location of the shared sub-character components, suggesting highly selective neuronal tuning to whole characters. The consistent neural profile across the entire ventral visual cortex indicates that Chinese characters are represented as mutually distinctive wholes rather than combinations of sub-character components, which presents a salient contrast to the left-lateralized, simple-to-complex neural representations of alphabetic words. Our findings thus revealed the cultural modulation effect on both local neuronal activity patterns and functional anatomical regions associated with written symbol recognition. Moreover, the cross-language discrepancy in written symbol recognition mechanism might stem from the language-specific early-stage learning experience.

  20. Changes in the function of the inhibitory neurotransmitter system in the rat brain following subchronic inhalation exposure to 1-bromopropane.

    Science.gov (United States)

    Ueno, Susumu; Yoshida, Yasuhiro; Fueta, Yukiko; Ishidao, Toru; Liu, Jiqin; Kunugita, Naoki; Yanagihara, Nobuyuki; Hori, Hajime

    2007-03-01

    1-Bromopropane (1-BP) has been widely used as a cleaning agent and a solvent in industries, but the central neurotoxicity of 1-BP remains to be clarified. In the present study, we investigated the effects of subchronic inhalation exposure to 1-BP vapor on the function of the inhibitory neurotransmitter system mediated by gamma-aminobutyric acid (GABA) in the rat brain. Male Wistar rats were exposed to 1-BP vapor for 12 weeks (6h/day, 5 days/week) at a concentration of 400 ppm, and, in order to investigate the expression and function of brain GABA type A (GABAA) receptors, total/messenger RNA was prepared from the neocortex, hippocampus, and cerebellum of the control and 1-BP-exposed rats. Moreover, hippocampal slices were prepared, and the population spike (PS) amplitude and the slope of the field excitatory postsynaptic potential (fEPSP) were investigated in the paired-pulse configuration of the extracellular recording technique. Using the Xenopus oocyte expression system, we compared GABA concentration-response curves obtained from oocytes injected with brain subregional mRNAs of control and 1-BP exposed rats, and observed no significant differences in apparent GABA affinity. On the other hand, paired-pulse inhibition of PS amplitude was significantly decreased in the hippocampal dentate gyrus (DG) by exposure to 1-BP, without any effect on the paired-pulse ratio of the fEPSP slopes, suggesting neuronal disinhibition in the DG. Moreover, RT-PCR analysis indicated decreased levels of GABAA receptor beta3 and delta subunit mRNAs in the hippocampus of 1-BP-exposed rats. These results demonstrate that subchronic inhalation exposure to 1-BP vapor reduces the function of the hippocampal GABAergic system, which could be due to changes in the expression and function of GABAA receptors, especially the delta subunit-containing GABAA receptors.

  1. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  2. Neural network based system for script identification in Indian documents

    Indian Academy of Sciences (India)

    S Basavaraj Patil; N V Subbareddy

    2002-02-01

    The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage the document image is dilated using 3 × 3 masks in horizontal, vertical, right diagonal, and left diagonal directions. In the next stage, average pixel distribution is found in these resulting images. The modular network is a combination of separately trained feedforward neural network classifiers for each script. The system recognizes 64 × 64 pixel document images. In the next level, the system is modified to perform on single word-document images in the same three scripts. Modified system includes a pre-processor, modified feature extractor and probabilistic neural network classifier. Pre-processor segments the multi-script multi-lingual document into individual words. The feature extractor receives these word-document images of variable size and still produces the discriminative features employed by the probabilistic neural classifier. Experiments are conducted on a manually developed database of document images of size 64 × 64 pixels and on a database of individual words in the three scripts. The results are very encouraging and prove the effectiveness of the approach.

  3. Frontotemporal neural systems supporting semantic processing in Alzheimer's disease.

    Science.gov (United States)

    Peelle, Jonathan E; Powers, John; Cook, Philip A; Smith, Edward E; Grossman, Murray

    2014-03-01

    We hypothesized that semantic memory for object concepts involves both representations of visual feature knowledge in modality-specific association cortex and heteromodal regions that are important for integrating and organizing this semantic knowledge so that it can be used in a flexible, contextually appropriate manner. We examined this hypothesis in an fMRI study of mild Alzheimer's disease (AD). Participants were presented with pairs of printed words and asked whether the words matched on a given visual-perceptual feature (e.g., guitar, violin: SHAPE). The stimuli probed natural kinds and manufactured objects, and the judgments involved shape or color. We found activation of bilateral ventral temporal cortex and left dorsolateral prefrontal cortex during semantic judgments, with AD patients showing less activation of these regions than healthy seniors. Moreover, AD patients showed less ventral temporal activation than did healthy seniors for manufactured objects, but not for natural kinds. We also used diffusion-weighted MRI of white matter to examine fractional anisotropy (FA). Patients with AD showed significantly reduced FA in the superior longitudinal fasciculus and inferior frontal-occipital fasciculus, which carry projections linking temporal and frontal regions of this semantic network. Our results are consistent with the hypothesis that semantic memory is supported in part by a large-scale neural network involving modality-specific association cortex, heteromodal association cortex, and projections between these regions. The semantic deficit in AD thus arises from gray matter disease that affects the representation of feature knowledge and processing its content, as well as white matter disease that interrupts the integrated functioning of this large-scale network.

  4. Draxin, an axon guidance protein, affects chick trunk neural crest migration.

    Science.gov (United States)

    Su, Yuhong; Naser, Iftekhar B; Islam, Shahidul M; Zhang, Sanbing; Ahmed, Giasuddin; Chen, Sandy; Shinmyo, Yohei; Kawakami, Minoru; Yamamura, Ken-ichi; Tanaka, Hideaki

    2009-12-01

    The neural crest is a multipotent population of migratory cells that arises in the central nervous system and subsequently migrates along defined stereotypic pathways. In the present work, we analyzed the role of a repulsive axon guidance protein, draxin, in the migration of neural crest cells. Draxin is expressed in the roof plate of the chick trunk spinal cord and around the early migration pathway of neural crest cells. Draxin modulates chick neural crest cell migration in vitro by reducing the polarization of these cells. When exposed to draxin, the velocity of migrating neural crest cells was reduced, and the cells changed direction so frequently that the net migration distance was also reduced. Overexpression of draxin also caused some early migrating neural crest cells to change direction to the dorsolateral pathway in the chick trunk region, presumably due to draxin's inhibitory activity. These results demonstrate that draxin, an axon guidance protein, can also affect trunk neural crest migration in the chick embryo.

  5. A Dynamic Effective Fault Tolerance System in Robotic Manipulator using a Hybrid Neural Network based Controller

    Directory of Open Access Journals (Sweden)

    G. Jiji

    2014-04-01

    Full Text Available Robot manipulator play important role in the field of automobile industry, mainly it is used in gas welding application and manufacturing and assembling of motor parts. In complex trajectory, on each joint the speed of the robot manipulator is affected. For that reason, it is necessary to analyze the noise and vibration of robot's joints for predicting faults also improve the control precision of robotic manipulator. In this study we will propose a new fault detection system for Robot manipulator. The proposed hybrid fault detection system is designed based on fuzzy support vector machine and Artificial Neural Networks (ANNs. In this system the decouple joints are identified and corrected using fuzzy SVM, here non-linear signal are used for complete process and treatment, the Artificial Neural Networks (ANNs are used to detect the free-swinging and locked joint of the robot, two types of neural predictors are also employed in the proposed adaptive neural network structure. The simulation results of a hybrid controller demonstrate the feasibility and performance of the methodology.

  6. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

    Science.gov (United States)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K. Y. Michael; Zhou, Changsong

    2016-01-01

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  7. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

  8. 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.

  9. Conditional entropies, phase synchronization and changes in the directionality of information flow in neural systems

    Science.gov (United States)

    Zochowski, Michal; Dzakpasu, Rhonda

    2004-03-01

    We devised a novel measure that dynamically evaluates temporal interdependences between two coupled units based on the properties of the distributions of their relative interevent intervals. We investigate its properties on the system of two coupled non-identical Rössler oscillators and a system of non-identical Hindmarsh-Rose models of thalamocortical neurons and show that the measure highlights the properties of phase synchronization observed in those two systems. We postulate that the observed properties of the phase lag, in conjunction with the experimentally observed activity-dependent synaptic modification in the neural systems, may drive the changes of the direction of information flow in a neural network, and thus the measure can play an important role in assessing those changes.

  10. H∞ output tracking control of discrete-time nonlinear systems via standard neural network models.

    Science.gov (United States)

    Liu, Meiqin; Zhang, Senlin; Chen, Haiyang; Sheng, Weihua

    2014-10-01

    This brief proposes an output tracking control for a class of discrete-time nonlinear systems with disturbances. A standard neural network model is used to represent discrete-time nonlinear systems whose nonlinearity satisfies the sector conditions. H∞ control performance for the closed-loop system including the standard neural network model, the reference model, and state feedback controller is analyzed using Lyapunov-Krasovskii stability theorem and linear matrix inequality (LMI) approach. The H∞ controller, of which the parameters are obtained by solving LMIs, guarantees that the output of the closed-loop system closely tracks the output of a given reference model well, and reduces the influence of disturbances on the tracking error. Three numerical examples are provided to show the effectiveness of the proposed H∞ output tracking design approach.

  11. Adaptive Neural Tracking Control for a Class of Nonlinear Systems With Dynamic Uncertainties.

    Science.gov (United States)

    Wang, Huanqing; Shi, Peng; Li, Hongyi; Zhou, Qi

    2016-09-22

    This paper considers the problem of adaptive neural control of nonlower triangular nonlinear systems with unmodeled dynamics and dynamic disturbances. The design difficulties appeared in the unmodeled dynamics and nonlower triangular form are handled with a dynamic signal and a variable partition technique for the nonlinear functions of all state variables, respectively. It is shown that the proposed controller is able to ensure the semi-global boundedness of all signals of the resulting closed-loop system. Furthermore, the system output is ensured to converge to a small domain of the given trajectories. The main advantage about this research is that a neural networks-based tracking control method is developed for uncertain nonlinear systems with unmodeled dynamics and nonlower triangular form. Simulation results demonstrate the feasibility of the newly presented design techniques.

  12. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia)); Sobajic, D.J.; Pao, Y.-H. (Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States))

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  13. Adaptive Neural Network Output Feedback Tracking Control for a Class of Complicated Agricultural Mechanical Systems

    Directory of Open Access Journals (Sweden)

    Hui Hu

    2015-07-01

    Full Text Available The study presents an adaptive neural network output feedback tracking control scheme for a class of complicated agricultural mechanical systems. The scheme includes a dynamic gain observer to estimate the un-measurable states of the system. The main advantages of the authors scheme are that by introducing non-separation principle design neural network controller and the observer gain are simultaneously tuned according to output tracking error, the semi-globally ultimately bounded of output tracking error and all the states in the closed-loop system can be achieved by Lyapunov approach. With the universal approximation property of NN and the simultaneous parametrisation, no Lipschitz assumption and SPR condition are employed which makes the system construct simple. Finally the simulation results are presented to demonstrate the efficiency of the control scheme.

  14. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  15. Time-Delay Neural Network for Smart MIMO Channel Estimation in Downlink 4G-LTE-Advance System

    OpenAIRE

    Nirmalkumar S. Reshamwala; Pooja S. Suratia; Satish K. Shah

    2014-01-01

    Long-Term Evolution (LTE) is the next generation of current mobile telecommunication networks. LTE has a new flat radio-network architecture and significant increase in spectrum efficiency. In this paper, main focus on throughput performance analysis of robust MIMO channel estimators for Downlink Long Term Evolution-Advance (DL LTE-A)-4G system using three Artificial Neural Networks: Feed-forward neural network (FFNN), Cascade-forward neural network (CFNN) and Time-Delay neural network (TDNN) a...

  16. Prediction of a model enzymatic acidolysis system using neural networks

    Directory of Open Access Journals (Sweden)

    Güven, Aytaç

    2008-12-01

    Full Text Available A model for the acidolysis of trinolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase was presented in this study. A neural networks (NN based model was developed for the prediction of the concentrations of the major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO 1,3-dipalmitoyl-2-oleoyl-glycerol (POP and triolein (OOO. Substrate ratio (SR, reaction temperature (T and reaction time (t were used as input parameters. The optimal architecture of the proposed NN model, which consists of one input layer with three inputs, one hidden layer with seven neurons and one output layer with three outputs, wass able to predict the reaction products concentration with a mean square error (MSE of less than 1.5 and R2 of 0.999. and explicit formulation of the proposed NN is presented. Considerable good performance is achieved in modeling the acidolysis reaction using neuronal networks.En este estudio se presenta un modelo para la acidólisis de la trilinoleina y el ácido palmítico mediante la catálisis con una lipasa específica sn-1,3 inmovilizada. Un modelo basado en redes neuronales (NN ha sido desarrollado para la predicción de la concentración de los principales productos de esta reacción (1-palmitoil-2,3-oleoil-glicerol (POO, 1,3-dipalmitoil-2-oleoil-glicerol (POP y trioleina (OOO. Se han usado como parámetros de entrada: la proporción del sustrato (SR, la temperatura de reacción (T y el tiempo de reacción (t. La arquitectura óptima del modelo de NN propuesto, que consiste en una capa de entrada con tres entradas, una capa oculta con siete neuronas y una capa de salida con tres salidas, fue capaz de predecir la concentración de los productos de reacción con un error cuadrático medio (MSE de menos de 1.5 y una R2 de 0.999 . Se presenta una formulación explícita del modelo NN propuesto. Se obtienen muy buenos resultados en la predicción de la reacciones de acidólisis mediante el uso de

  17. A four-channel microelectronic system for neural signal regeneration

    Energy Technology Data Exchange (ETDEWEB)

    Xie Shushan; Wang Zhigong; Li Wenyuan [Institute of RF- and OE-ICs, Southeast University, Nanjing 210096 (China); Lue Xiaoying; Pan Haixian, E-mail: zgwang@seu.edu.c [State Key Laboratory of Bio-Electronics, Southeast University, Nanjing 210096 (China)

    2009-12-15

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions, and the two OPAs are a low-noise low-power two-stage OPA and a constant-g{sub m} RTR input and output OPA. The system has been realized in CSMC 0.5-{mu}m CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration. (semiconductor integrated circuits)

  18. Characterization of nonlinear dynamic systems using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Urbina, A. [Univ. of Texas, El Paso, TX (United States); Hunter, N.F. [Los Alamos National Lab., NM (United States). Engineering Science and Analysis Div.; Paez, T.L. [Sandia National Labs., Albuquerque, NM (United States). Experimental Structural Dynamics Dept.

    1998-12-01

    The efficient characterization of nonlinear systems is an important goal of vibration and model testing. The authors build a nonlinear system model based on the acceleration time series response of a single input, multiple output system. A series of local linear models are used as a template to train artificial neutral networks (ANNs). The trained ANNs map measured time series responses into states of a nonlinear system. Another NN propagates response states in time, and a third ANN inverts the original map, transforming states into acceleration predictions in the measurement domain. The technique is illustrated using a nonlinear oscillator, in which quadratic and cubic stiffness terms play a major part in the system`s response. Reasonable maps are obtained for the states, and accurate, long-term response predictions are made for data outside the training data set.

  19. A four-channel microelectronic system for neural signal regeneration

    Institute of Scientific and Technical Information of China (English)

    Xie Shushan; Wang Zhigong; Lü Xiaoying; Li Wenyuan; Pan Haixian

    2009-01-01

    This paper presents a microelectronic system which is capable of making a signal record and functional electric stimulation of an injured spinal cord. As a requirement of implantable engineering for the regeneration microelectronic system, the system is of low noise, low power, small size and high performance. A front-end circuit and two high performance OPAs (operational amplifiers) have been designed for the system with different functions,and the two OPAs are a low-noise low-power two-stage OPA and a constant-g_m RTR input and output OPA. The system has been realized in CSMC 0.5-μm CMOS technology. The test results show that the system satisfies the demands of neuron signal regeneration.

  20. Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

    OpenAIRE

    Reyes-Reyes J.; yu W.; Poznyak A. S.

    2000-01-01

    In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN), containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback con...

  1. Neural networks for modelling and control of a non-linear dynamic system

    OpenAIRE

    Murray-Smith, R.; Neumerkel, D.; Sbarbaro-Hofer, D.

    1992-01-01

    The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved ...

  2. Dynamic Structure Neural Networks for Stable Adaptive Control of Nonlinear Systems

    OpenAIRE

    Fabri, S.; Kadirkamanathan, V.

    1994-01-01

    An adaptive control technique, using dynamic structure Gaussian radical basis function neural networks, that grow in time according to the location of the system's state in space is presented for the affine class of nonlinear systems having unknown or partially known dynamics. The method results in a network that is economic in terms of network size, for cases where the state spans only a small subset of state space, by utilising less basis functions than would have been the case if basis fun...

  3. Current-mode subthreshold MOS circuits for analog VLSI neural systems

    Science.gov (United States)

    Andreou, Andreas G.; Boahen, Kwabena A.; Pouliquen, Philippe O.; Pavasovic, Aleksandra; Jenkins, Robert E.

    1991-03-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  4. Current-mode subthreshold MOS circuits for analog VLSI neural systems.

    Science.gov (United States)

    Andreou, A G; Boahen, K A; Pouliquen, P O; Pavasovic, A; Jenkins, R E; Strohbehn, K

    1991-01-01

    An overview of the current-mode approach for designing analog VLSI neural systems in subthreshold CMOS technology is presented. Emphasis is given to design techniques at the device level using the current-controlled current conveyor and the translinear principle. Circuits for associative memory and silicon retina systems are used as examples. The design methodology and how it relates to actual biological microcircuits are discussed.

  5. Different Avalanche Behaviors in Different Specific Areas of a System Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xiao-Wei; CHEN Tian-Lun

    2003-01-01

    Based on the standard self-organizing map (SOM) neural network model and an integrate-and-fire mecha-nism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of modelneural populations. We find power-law distribution behavior of avalanche size in our model. But more importantly, wefind there are different avalanche distribution behaviors in different specific areas of our system, which are formed by thetopological learning process of the SOM net.

  6. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  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. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to microenv

  9. Plasticity and Neural Stem Cells in the Enteric Nervous System

    NARCIS (Netherlands)

    Schaefer, Karl-Herbert; Van Ginneken, Chris; Copray, Sjef

    2009-01-01

    The enteric nervous system (ENS) is a highly organized part of the autonomic nervous system, which innervates the whole gastrointestinal tract by several interconnected neuronal networks. The ENS changes during development and keeps throughout its lifespan a significant capacity to adapt to

  10. Toward an analog neural substrate for production systems

    NARCIS (Netherlands)

    Simen, P.; van Vugt, M. K.; Balci, F.; Freestone, D.; Polk, T.; Salvucci, D. D.; Gunzelmann, G

    2010-01-01

    Symbolic, rule-based systems seem essential for modeling high-level cognition. Subsymbolic dynamical systems, in con- trast, seem essential for modeling low-level perception and action, and can be mapped more easily onto the brain. Here we review existing work showing that critical features of symbo

  11. Neural systems analysis of decision making during goal-directed navigation.

    Science.gov (United States)

    Penner, Marsha R; Mizumori, Sheri J Y

    2012-01-01

    The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors.

  12. Transient Stability Enhancement of Power Systems by Lyapunov-Based Recurrent Neural Networks UPFC Controllers

    Science.gov (United States)

    Chu, Chia-Chi; Tsai, Hung-Chi; Chang, Wei-Neng

    A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

  13. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  14. Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

    Science.gov (United States)

    Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

    2015-01-01

    Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

  15. A wireless transmission neural interface system for unconstrained non-human primates

    Science.gov (United States)

    Fernandez-Leon, Jose A.; Parajuli, Arun; Franklin, Robert; Sorenson, Michael; Felleman, Daniel J.; Hansen, Bryan J.; Hu, Ming; Dragoi, Valentin

    2015-10-01

    Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7-5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

  16. Hybrid information privacy system: integration of chaotic neural network and RSA coding

    Science.gov (United States)

    Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.

    2005-03-01

    Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.

  17. Neural Network Signal Processing Approach for Damage Assessment in Fiberoptic Smart Material Systems and Sructures①②

    Institute of Scientific and Technical Information of China (English)

    TUYaqing; HUANGShanglian

    1997-01-01

    An approach by using neural network signal processing in associate with embedded fiberoptic sensing array for the newly developed“smart material systems and structures” is discussed in this paper.The principle,structure of this approach and suitable neural network algorithms are described.The results of simulation experiments are also given.

  18. Experimental analysis of a Lotka-Volterra neural network for classification

    Science.gov (United States)

    Sukhu, Christopher L.; Stanton, Joseph; Aylesworth, Marc

    2015-06-01

    An experimental study of a neural network modeled by an adaptive Lotka-Volterra system follows. With totally inhibitory connections, this system can be embedded in a simple classification network. This network is able to classify and monitor its inputs in a spontaneous nonlinear fashion without prior training. We describe a framework for leveraging this behavior through an example involving breast cancer diagnosis.

  19. Inhibitory avoidance acquisition in adult rats exposed to a combination of ethanol and methylmercury during central nervous system development.

    Science.gov (United States)

    Maia, Cristiane do Socorro Ferraz; Ferreira, Vania Maria Moraes; Diniz, Júlia Silva Valério; Carneiro, Fabiana Pirani; de Sousa, João Batista; da Costa, Edmar Tavares; Tomaz, Carlos

    2010-08-25

    Previous studies have shown that combined exposure to ethanol (EtOH) and methylmercury (MeHg) in rats during central nervous system development produces several behavioural impairments. This present study was done to investigate inhibitory avoidance acquisition and panic-like disorders in rats in an elevated T-maze (ETM) model of anxiety. Pregnant rats received tap water or EtOH at 22.5% (w/v) (6.5 g/kg per day, by gavage) during pregnancy and lactation. On the 15th day of pregnancy, half of each group received MeHg (8 mg/kg, by gavage). Adult offspring intoxicated by both EtOH + MeHg showed an increase in the ETM re-exposure time. Upon analysis of the enclosed arms latency in baseline and avoidance 1 session it was observed that the rats spent less time inside the arm, suggesting impairment in their short-term memory. The escape latency decreased for EtOH + MeHg and also for EtOH and MeHg groups, suggesting panic-like behaviour. After 24-h and 7-day trials (tests and retests), MeHg and EtOH + MeHg groups had their latency in the enclosed arm reduced with the exception of the EtOH group, revealing memory impairment. Upon analysis of the risk assessment, animals treated with EtOH + MeHg were the only ones to show a decrease in all evaluation stages. This study demonstrates that the exposure to both EtOH and MeHg has an impact on memory and panic-related behaviours, leading to the assertion that this association of toxicants should be studied more in detail to clarify the precise mechanisms of these pharmacological effects.

  20. Robust MPC for a non-linear system - a neural network approach

    Science.gov (United States)

    Luzar, Marcel; Witczak, Marcin

    2014-12-01

    The aim of the paper is to design a robust actuator fault-tolerant control for a non-linear discrete-time system. Considered system is described by the Linear Parameter-Varying (LPV) model obtained with recurrent neural network. The proposed solution starts with a discretetime quasi-LPV system identification using artificial neural network. Subsequently, the robust controller is proposed, which does not take into account actuator saturation level and deals with the previously estimated faults. To check if the compensation problem is feasible, the robust invariant set is employed, which takes into account actuator saturation level. When the current state does not belong to the set, then a predictive control is performed in order to make such set larger. This makes it possible to increase the domain of attraction, which makes the proposed methodology an efficient solution for the fault-tolerant control. The last part of the paper presents an experimental results regarding wind turbines.

  1. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

    Science.gov (United States)

    Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

    2009-01-01

    This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  2. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

    Directory of Open Access Journals (Sweden)

    Min-Seok Park

    2009-10-01

    Full Text Available This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

  3. Neural Network Inverse Model Control Strategy: Discrete-Time Stability Analysis for Relative Order Two Systems

    Directory of Open Access Journals (Sweden)

    M. A. Hussain

    2014-01-01

    Full Text Available This paper discusses the discrete-time stability analysis of a neural network inverse model control strategy for a relative order two nonlinear system. The analysis is done by representing the closed loop system in state space format and then analyzing the time derivative of the state trajectory using Lyapunov’s direct method. The analysis shows that the tracking output error of the states is confined to a ball in the neighborhood of the equilibrium point where the size of the ball is partly dependent on the accuracy of the neural network model acting as the controller. Simulation studies on the two-tank-in-series system were done to complement the stability analysis and to demonstrate some salient results of the study.

  4. Simulation and stability analysis of neural network based control scheme for switched linear systems.

    Science.gov (United States)

    Singh, H P; Sukavanam, N

    2012-01-01

    This paper proposes a new adaptive neural network based control scheme for switched linear systems with parametric uncertainty and external disturbance. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network is employed to learn this upper bound. The adaptive learning algorithm is derived from Lyapunov stability analysis so that the system response under arbitrary switching laws is guaranteed uniformly ultimately bounded. A comparative simulation study with robust controller given in [Zhang L, Lu Y, Chen Y, Mastorakis NE. Robust uniformly ultimate boundedness control for uncertain switched linear systems. Computers and Mathematics with Applications 2008; 56: 1709-14] is presented.

  5. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  6. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

    Science.gov (United States)

    Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y.-F.; Chang, F.-J.

    2011-01-01

    Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

  7. Robust adaptive neural control of uncertain pure-feedback nonlinear systems

    Science.gov (United States)

    Sun, Gang; Wang, Dan; Peng, Zhouhua; Wang, Hao; Lan, Weiyao; Wang, Mingxin

    2013-05-01

    In this paper, a robust adaptive neural control design approach is presented for a class of uncertain pure-feedback nonlinear systems. To reduce the complexity of the both controller structure and computation, only one neural network is used to approximate the lumped unknown function of the system at the last step of the recursive design process. By this approach, the complexity growing problem existing in conventional methods can be eliminated completely. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness and merits of the proposed approach.

  8. Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

    Institute of Scientific and Technical Information of China (English)

    Chuntao LI; Yonghong TAN

    2004-01-01

    An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semi- global stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.

  9. Application of Neural Network in Simple Tool Wear Monitoring and Indentification System in MDF Milling

    Directory of Open Access Journals (Sweden)

    Marcin Zbieć

    2011-03-01

    Full Text Available This paper deals with simple neural network-based diagnostic system, applied to tool wear prediction in MDF milling. Ten tools were used for the test, and each one was consequently worn in the process of MDF milling. During the wearing process, the key process parameters were measured, such as cutting and thrust forces, temperature and power consumption. The neural network-based system was used for tool wear prediction of all the tools except the fi rst one, based on data collected during the previous attempts. The test has shown that the proposed system has good prediction accuracy and that it could be a useful tool in the optimization of the woodworking process.

  10. Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear Systems Control Using Stable Simultaneous Perturbation Stochastic Approximation Algorithm

    Directory of Open Access Journals (Sweden)

    Ching-Hung Lee

    2011-01-01

    Full Text Available This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function, for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results for the chaotic system identification and the control of Chua's chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.

  11. Design of Neural Network Variable Structure Reentry Control System for Reusable Launch Vehicle

    Institute of Scientific and Technical Information of China (English)

    HU Wei-jun; ZHOU Jun

    2008-01-01

    A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory. The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method. High precise and robust tracking of required attitude angles can be achieved in complicated air space. A mathematical model of reusable launch vehicle is pre-sented first, and then a controller of flight system is presented. Base on the mathematical model, the controller is divided into two parts: variable-structure controller and neural network module which is used to modify the parameters of con-troller. This control system decouples the lateral/directional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles. After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters. The final simulation shows that the control system has a good accurate, robust and de-coupled tracking performance. The stable state error is less than 1°, and the overshoot is less than 5%.

  12. Optimal design of systems that evolve over time using neural networks

    Science.gov (United States)

    Nolan, Michael K.

    2007-04-01

    Design optimization is challenging when the number of variables becomes large. One method of addressing this problem is to use pattern recognition to decrease the solution space in which the optimizer searches. Human "common sense" is used by designers to narrow the scope of search to a confined area defined by patterns conforming to likely solution candidates. However, computer-based optimization generally does not apply similar heuristics. In this paper, a system is presented that recognizes patterns and adjusts its search for optimal solutions based on these patterns. A design problem was selected that requires the optimization algorithm to assess designs that evolve over time. A small sensor network design is evolved into a larger sensor network design. Optimal design solutions for the small network do not necessarily lead to optimal solutions for the larger network. Systems that are well-positioned to evolve have characteristics that distinguish themselves from systems that are not well-positioned to evolve. In this study, a neural network was able to recognize a pattern whereby flexible sensor networks evolved more successfully than less flexible networks. The optimizing algorithm used this pattern to select candidate systems that showed promise for evolution. A genetic algorithm assisted by a neural network achieved better performance than an unassisted genetic algorithm did. This thesis advocates the merit of neural network use in multi-objective system design optimization and to lay a basis for future study.

  13. Transient Stability Assessment of a Power System Using Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Noor I.A. Wahab

    2008-01-01

    Full Text Available This study presents transient stability assessment of electrical power system using Probabilistic Neural Network (PNN and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN in which PNN is used as a classifier to determine whether the power system is stable or unstable. Principle component analysis is applied to extract useful input features to the PNN so that training time of the PNN can be reduced. To verify the effectiveness of the proposed PNN method, it is compared with the multi layer perceptron neural network. Results show that the PNN gives faster and more accurate transient stability assessment compared to the multi layer perceptron neural network in terms of classification results.

  14. Miniaturized neural sensing and optogenetic stimulation system for behavioral studies in the rat

    Science.gov (United States)

    Kim, Min Hyuck; Nam, Ilho; Ryu, Youngki; Wellman, Laurie W.; Sanford, Larry D.; Yoon, Hargsoon

    2015-04-01

    Real time sensing of localized electrophysiological and neurochemical signals associated with spontaneous and evoked neural activity is critically important for understanding neural networks in the brain. Our goal is to enhance the functionality and flexibility of a neural sensing and stimulation system for the observation of brain activity that will enable better understanding from the level of individual cells to that of global structures. We have thus developed a miniaturized electronic system for in-vivo neurotransmitter sensing and optogenetic stimulation amenable to behavioral studies in the rat. The system contains a potentiostat, a data acquisition unit, a control unit, and a wireless data transfer unit. For the potentiostat, we applied embedded op-amps to build single potential amperometry for electrochemical sensing of dopamine. A light emitting diode is controlled by a microcontroller and pulse width modulation utilized to control optogenetic stimulation within a sub-millisecond level. In addition, this proto-typed electronic system contains a Bluetooth module for wireless data communication. In the future, an application-specific integrated circuit (ASIC) will be designed for further miniaturization of the system.

  15. An Automatic System of Vehicle Number-Plate Recognition Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper presents an automatic system of vehicle number-plate recognition based on neural networks. In this system, location of number-plate and recognition of characters in number-plate can be automatically completed. Pixel colors of Number-plate area are classified using neural network, then color features are extracted by analyzing scanning lines of the cross-section of number-plate. It takes full use of number-plate color features to locate number plate. Characters in number-plate can be effectively recognized using the neural networks. Experimental results show that the correct rate of number-plate location is close to 100%, and the time of number-plate location is less than 1 second. Moreover, recognition rate of characters is improved due to the known number-plate type. It is also observed that this system is not sensitive to variations of weather, illumination and vehicle speed. In addition, and also the size of number-plate need not to be known in prior. This system is of crucial significance to apply and spread the automatic system of vehicle number-plate recognition.

  16. Investigation of neural-net based control strategies for improved power system dynamic performance

    Energy Technology Data Exchange (ETDEWEB)

    Sobajic, D.J. [Electric Power Research Institute, Palo Alto, CA (United States)

    1995-12-31

    The ability to accurately predict the behavior of a dynamic system is of essential importance in monitoring and control of complex processes. In this regard recent advances in neural-net base system identification represent a significant step toward development and design of a new generation of control tools for increased system performance and reliability. The enabling functionality is the one of accurate representation of a model of a nonlinear and nonstationary dynamic system. This functionality provides valuable new opportunities including: (1) The ability to predict future system behavior on the basis of actual system observations, (2) On-line evaluation and display of system performance and design of early warning systems, and (3) Controller optimization for improved system performance. In this presentation, we discuss the issues involved in definition and design of learning control systems and their impact on power system control. Several numerical examples are provided for illustrative purpose.

  17. Neural systems and hormones mediating attraction to infant and child faces.

    Science.gov (United States)

    Luo, Lizhu; Ma, Xiaole; Zheng, Xiaoxiao; Zhao, Weihua; Xu, Lei; Becker, Benjamin; Kendrick, Keith M

    2015-01-01

    We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed "Kindchenschema" or "baby schema," and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It then provides details of the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and the neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cuteness in infant faces, with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses. Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention and attraction to infant cues in both sexes.

  18. Neural systems and hormones mediating attraction to infant and child faces

    Directory of Open Access Journals (Sweden)

    Lizhu eLuo

    2015-07-01

    Full Text Available We find infant faces highly attractive as a result of specific features which Konrad Lorenz termed Kindchenschema or baby schema, and this is considered to be an important adaptive trait for promoting protective and caregiving behaviors in adults, thereby increasing the chances of infant survival. This review first examines the behavioral support for this effect and physical and behavioral factors which can influence it. It next reviews the increasing number of neuroimaging and electrophysiological studies investigating the neural circuitry underlying this baby schema effect in both parents and non-parents of both sexes. Next it considers potential hormonal contributions to the baby schema effect in both sexes and then neural effects associated with reduced responses to infant cues in post-partum depression, anxiety and drug taking. Overall the findings reviewed reveal a very extensive neural circuitry involved in our perception of cutenessin infant faces with enhanced activation compared to adult faces being found in brain regions involved in face perception, attention, emotion, empathy, memory, reward and attachment, theory of mind and also control of motor responses.Both mothers and fathers also show evidence for enhanced responses in these same neural systems when viewing their own as opposed to another child. Furthermore, responses to infant cues in many of these neural systems are reduced in mothers with post-partum depression or anxiety or have taken addictive drugs throughout pregnancy. In general reproductively active women tend to rate infant faces as cuter than men, which may reflect both heightened attention to relevant cues and a stronger activation in their brain reward circuitry. Perception of infant cuteness may also be influenced by reproductive hormones with the hypothalamic neuropeptide oxytocin being most strongly associated to date with increased attention andattractionto infant cues in both sexes.

  19. Power prediction in mobile communication systems using an optimal neural-network structure.

    Science.gov (United States)

    Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J

    1997-01-01

    Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

  20. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  1. Confirmation of association of the macrophage migration inhibitory factor gene with systemic sclerosis in a large European population

    NARCIS (Netherlands)

    Bossini-Castillo, L.; Simeon, C.P.; Beretta, L.; Vonk, M.C.; Callejas-Rubio, J.L.; Espinosa, G.; Carreira, P.; Camps, M.T.; Rodriguez-Rodriguez, L.; Rodriguez-Carballeira, M.; Garcia-Hernandez, F.J.; Lopez-Longo, F.J.; Hernandez-Hernandez, V.; Saez-Comet, L.; Egurbide, M.V.; Hesselstrand, R.; Nordin, A.; Hoffmann-Vold, A.M.; Vanthuyne, M.; Smith, V.; Langhe, E. De; Kreuter, A.; Riemekasten, G.; Witte, T.J.M. de; Hunzelmann, N.; Voskuyl, A.E.; Schuerwegh, A.J.; Lunardi, C.; Airo, P.; Scorza, R.; Shiels, P.; Laar, J.M. van; Fonseca, C.; Denton, C.; Herrick, A.; Worthington, J.; Koeleman, B.P.; Rueda, B.; Radstake, T.R.D.J.; Martin, J.

    2011-01-01

    Objectives. The aim of this study was to confirm the implication of macrophage migration inhibitory factor (MIF) gene in SSc susceptibility or clinical phenotypes in a large European population. Methods. A total of 3800 SSc patients and 4282 healthy controls of white Caucasian ancestry from eight

  2. System control fuzzy neural sewage pumping stations using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Владлен Николаевич Кузнецов

    2015-06-01

    Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

  3. Dynamic neural network-based robust observers for uncertain nonlinear systems.

    Science.gov (United States)

    Dinh, H T; Kamalapurkar, R; Bhasin, S; Dixon, W E

    2014-12-01

    A dynamic neural network (DNN) based robust observer for uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the system states of high-order uncertain nonlinear systems through Lyapunov-based analysis. Simulations and experiments on a two-link robot manipulator are performed to show the effectiveness of the proposed method in comparison to several other state estimation methods.

  4. Filtering and Estimation of Vehicular Dead Reckoning System Based on Hopfield Neural Network

    Institute of Scientific and Technical Information of China (English)

    毕军; 付梦印; 张启鸿

    2003-01-01

    The algorithm of Hopfield neural network filtering and estimation is studied. The model of vehicular dead reckoning system fitting for the algorithm is constructed, and the design scheme of system filtering and estimation based on Hopfield network is proposed. Compared with Kalman filter, the algorithm does not require very precise system model and the prior knowledge of noise statistics and does not diverge easily. The simulation results show that the vehicular dead reckoning system based on Hopfield network filtering and estimation has the good position precision, and needn't require the inertial sensors with high precision. Therefore, the algorithm has the good practicability.

  5. Fuzzy-Neural Petri Net Distributed Control System Using Hybrid Wireless Sensor Network and CAN Fieldbus

    Directory of Open Access Journals (Sweden)

    Ali A. Abed

    2016-06-01

    Full Text Available The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS. The implementation of our wireless system involves the building of a wireless sensor network (WSN for data acquisition and controller area network (CAN protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID-like Fuzzy Neural Petri Net (FNPN system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.

  6. 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.

  7. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-01-01

    Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

  8. Decentralized direct adaptive neural network control for a class of interconnected systems

    Institute of Scientific and Technical Information of China (English)

    Zhang Tianping; Mei Jiandong

    2006-01-01

    The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

  9. An Improved Brain Tumour Classification System using Wavelet Transform and Neural Network.

    Science.gov (United States)

    Dhas, DAS; Madheswaran, M

    2015-06-09

    An improved brain tumour classification system using wavelet transform and neural network is developed and presented in this paper. The anisotropic diffusion filter is used for image denoising and the performance of oriented rician noise reducing anisotropic diffusion (ORNRAD) filter is validated. The segmentation of the denoised image is carried out by Fuzzy C-means clustering. The features are extracted using Symlet and Coiflet Wavelet transform and Levenberg Marquardt algorithm based neural network is used to classify the magnetic resonance imaging (MRI) images. This MRI classification technique is tested and analysed with the existing methodologies and its performance is found to be satisfactory with a classification accuracy of 93.02%. The developed system can assist the physicians for classifying the MRI images for better decision-making.

  10. A Comparison between Neural Networks and Wavelet Networks in Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    S. Ehsan Razavi

    2012-01-01

    Full Text Available In this study, identification of a nonlinear function will be presented by neural network and wavelet network methods. Behavior of a nonlinear system can be identified by intelligent methods. Two groups of the most common and at the same time the most effective of neural networks methods are multilayer perceptron and radial basis function that will be used for nonlinear system identification. The selected structure is series - parallel method that after network training by a series of training random data, the output is estimated and the nonlinear function is compared to a sinusoidal input. Then, wavelet network is used for identification and we will use Orthogonal Least Squares (OLS method for wavelet selection to reduce the volume of calculations and increase the convergence speed.

  11. 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.

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

    Science.gov (United States)

    Gao, Hui; Song, Yongduan; Wen, Changyun

    2016-08-24

    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 L[₀,∞]. 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.

  13. Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

    Institute of Scientific and Technical Information of China (English)

    WANG Yu-jia; ZHANG Ming-jun

    2002-01-01

    A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.

  14. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  15. Speed Control of Two-Mass System Using Neural Network Estimator

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kyo Beum [Korea University (Korea, Republic of); Song, Joong Ho; Choi, Ick; Kim, Kwang Bae [Korea Institute of Science and Technology (Korea, Republic of); Lee, Kwang Won [Aju Univeristy (Korea, Republic of)

    1999-03-01

    A new control scheme using a torsional torque estimator based on a neural network is proposed and investigated for improving control characteristics of the high-performance motion control system. This control method presents better performance in the corresponding speed vibration response, compared with the disturbance observer-based control method. This result comes from the fact that the proposed neural network estimator keeps the self-learning capability, whereas the disturbance observer-based torque estimator with low pass filter should adjust the time constant of the adopted filter according to the natural resonance frequency determined by considering the system parameters varied. The simulation results shows the validity of the proposed control scheme. (author). 13 refs., 13 figs., 1 tab.

  16. Neural Network Model Of The PXIE RFQ Cooling System and Resonant Frequency Response

    Energy Technology Data Exchange (ETDEWEB)

    Edelen, Auralee [Fermilab; Biedron, Sandra [Colorado State U., Fort Collins; Bowring, Daniel [Fermilab; Chase, Brian [Fermilab; Edelen, Jonathan [Fermilab; Milton, Stephen [Colorado State U., Fort Collins; Steimel, Jim [Fermilab

    2016-06-01

    As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.

  17. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  18. Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

    Energy Technology Data Exchange (ETDEWEB)

    Ondrej Linda; Milos Manic; Timothy R. McJunkin

    2011-08-01

    Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

  19. Nonruminant Nutrition Symposium: Involvement of gut neural and endocrine systems in pathological disorders of the digestive tract.

    Science.gov (United States)

    Furness, J B; Poole, D P

    2012-04-01

    The functioning of the gastrointestinal tract is under the control of the most extensive system of peripheral neurons in the body, the enteric nervous system, and the largest endocrine system of the body, the GEP endocrine system. The enteric nervous system in large mammals contains 500 million neurons, and the GEP endocrine system produces more than 30 hormones. Numerous enteric neuropathies affecting both humans and animals have been described and digestive disorders affect commercially important species, such as horses and cattle. The most severe enteric neuropathies (e.g., lethal white syndrome in horses or Hirschsprung's disease in humans) can be fatal. Also, horses with ileus or other digestive disorders are commonly euthanized. In this review we discuss examples of enteric neuropathies that affect agricultural animals and humans: prion disease, postoperative ileus, distal enteric aganglionosis, and infective diarrhea. Enteric neurons and glia are a location of prion proteins and are involved in transmission of the infection from gut to brain and brain to gut. Postoperative ileus is a complex disorder involving the local inhibitory effects of sympathetic nervous system activation and the release of opioids, presumably from enteric neurons. Intestinal inflammation, especially of the external muscle that includes enteric ganglia, also occurs in ileus. Congenital distal bowel aganglionosis, responsible for lethal white syndrome in horses, Hirschsprung's disease in humans, and similar conditions in mice and rats, is a fatal condition if untreated. Mutations of the same genes can cause the condition in each of these species. The only effective current treatment is surgical removal of the aganglionic bowel. Infectious diarrheas involve activation of enteric secretomotor neurons by pathogens and the toxins they produce, which causes substantial fluid loss. Strategies to target enteric neurons in the treatment of secretory diarrheas have not been developed. Disorders

  20. First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

    OpenAIRE

    Edelen, A. L.; Biedron, S. G.; Milton, S. V.; Edelen, J. P.

    2016-01-01

    At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operatio...