A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

Science.gov (United States)

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence

Online Recorded Data-Based Composite Neural Control of Strict-Feedback Systems With Application to Hypersonic Flight Dynamics.

Science.gov (United States)

Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun

2017-09-25

This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.

Equations for studies of feedback stabilization

International Nuclear Information System (INIS)

Boozer, A.H.

1998-01-01

Important ideal magnetohydrodynamic (MHD) instabilities grow slowly when a conducting wall surrounds a toroidal plasma. Feedback stabilization of these instabilities may be required for tokamaks and other magnetic confinement concepts to achieve adequate plasma pressure and self-driven current for practical fusion power. Equations are derived for simulating feedback stabilization, which require the minimum information about an ideal plasma for an exact analysis. The equations are solved in the approximation of one unstable mode, one wall circuit, one feedback circuit, and one sensor circuit. The analysis based on a single unstable mode is shown to be mathematically equivalent to the standard analysis of feedback of the axisymmetric vertical instability of tokamaks. Unlike that analysis, the method presented here applies to multiple modes that are coupled by the wall and to arbitrary toroidal mode numbers. copyright 1998 American Institute of Physics

Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method.

Science.gov (United States)

Li, Xuanying; Li, Xiaotong; Hu, Cheng

2017-12-01

In this paper, without transforming the second order inertial neural networks into the first order differential systems by some variable substitutions, asymptotic stability and synchronization for a class of delayed inertial neural networks are investigated. Firstly, a new Lyapunov functional is constructed to directly propose the asymptotic stability of the inertial neural networks, and some new stability criteria are derived by means of Barbalat Lemma. Additionally, by designing a new feedback control strategy, the asymptotic synchronization of the addressed inertial networks is studied and some effective conditions are obtained. To reduce the control cost, an adaptive control scheme is designed to realize the asymptotic synchronization. It is noted that the dynamical behaviors of inertial neural networks are directly analyzed in this paper by constructing some new Lyapunov functionals, this is totally different from the traditional reduced-order variable substitution method. Finally, some numerical simulations are given to demonstrate the effectiveness of the derived theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Asymptotic stabilization of nonlinear systems using state feedback

International Nuclear Information System (INIS)

D'Attellis, Carlos

1990-01-01

This paper studies the design of state-feedback controllers for the stabilization of single-input single-output nonlinear systems x = f(x) + g(x)u, y = h(x). Two approaches for the stabilization problem are given; the asymptotic stability is achieved by means of: a) nonlinear state feedback: two nonlinear feedbacks are used; the first separates the system in a controllable linear part and in the zeros-dynamic part. The second feedback generates an asymptotically stable equilibrium on the manifold where this dynamics evolves; b) nonlinear dynamic feedback: conditions are established under which the system can follow the output of a completely controllable bilinear system which uses bounded controls. This fact enables the system to reach, using bounded controls too, a desired output value in finite time. As this value corresponds to a state that lays in the attraction basin of a stable equilibrium with the same output, the system evolves to that point. The two methods are illustrated by examples. (Author) [es

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

Science.gov (United States)

Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward

2016-10-01

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

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

Science.gov (United States)

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

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

Stability of Neutral Fractional Neural Networks with Delay

Institute of Scientific and Technical Information of China (English)

LI Yan; JIANG Wei; HU Bei-bei

2016-01-01

This paper studies stability of neutral fractional neural networks with delay. By introducing the definition of norm and using the uniform stability, the sufficient condition for uniform stability of neutral fractional neural networks with delay is obtained.

Neural correlates of anticipation and processing of performance feedback in social anxiety.

Science.gov (United States)

Heitmann, Carina Y; Peterburs, Jutta; Mothes-Lasch, Martin; Hallfarth, Marlit C; Böhme, Stephanie; Miltner, Wolfgang H R; Straube, Thomas

2014-12-01

Fear of negative evaluation, such as negative social performance feedback, is the core symptom of social anxiety. The present study investigated the neural correlates of anticipation and perception of social performance feedback in social anxiety. High (HSA) and low (LSA) socially anxious individuals were asked to give a speech on a personally relevant topic and received standardized but appropriate expert performance feedback in a succeeding experimental session in which neural activity was measured during anticipation and presentation of negative and positive performance feedback concerning the speech performance, or a neutral feedback-unrelated control condition. HSA compared to LSA subjects reported greater anxiety during anticipation of negative feedback. Functional magnetic resonance imaging results showed deactivation of medial prefrontal brain areas during anticipation of negative feedback relative to the control and the positive condition, and medial prefrontal and insular hyperactivation during presentation of negative as well as positive feedback in HSA compared to LSA subjects. The results indicate distinct processes underlying feedback processing during anticipation and presentation of feedback in HSA as compared to LSA individuals. In line with the role of the medial prefrontal cortex in self-referential information processing and the insula in interoception, social anxiety seems to be associated with lower self-monitoring during feedback anticipation, and an increased self-focus and interoception during feedback presentation, regardless of feedback valence. © 2014 Wiley Periodicals, Inc.

Analytic robust stability analysis of SVD orbit feedback

CERN Document Server

Pfingstner, Jürgen

2012-01-01

Orbit feedback controllers are indispensable for the operation of modern particle accelerators. Many such controllers are based on the decoupling of the inputs and outputs of the system to be controlled with the help of the singular value decomposition (SVD controller). It is crucial to verify the stability of SVD controllers, also in the presence of mismatches between the used accelerator model and the real machine (robust stability problem). In this paper, analytical criteria for guaranteed stability margins of SVD orbit feedback systems for three different types of model mismatches are presented: scaling errors of actuators and BPMs (beam position monitors) and additive errors of the orbit response matrix. For the derivation of these criteria, techniques from robust control theory have been used, e.g the small gain theorem. The obtained criteria can be easily applied directly to other SVD orbit feedback systems. As an example, the criteria were applied to the orbit feedback system of the Compact Linear ...

Stability of a neural predictive controller scheme on a neural model

DEFF Research Database (Denmark)

Luther, Jim Benjamin; Sørensen, Paul Haase

2009-01-01

In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system.......In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue...... has not been addressed specifically for these controllers. On the other hand a number of results concerning the stability of receding horizon controllers on a nonlinear system exist. In this paper we present a proof of stability for a predictive controller controlling a neural network model...

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

Directory of Open Access Journals (Sweden)

Rong Mei

2017-01-01

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

Stability prediction of berm breakwater using neural network

Digital Repository Service at National Institute of Oceanography (India)

Mandal, S.; Rao, S.; Manjunath, Y.R.

In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

Strategies influence neural activity for feedback learning across child and adolescent development.

Science.gov (United States)

Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

2014-09-01

Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

Science.gov (United States)

Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Stability of longitudinal bunch length feedback for heavy-ion synchrotrons

Directory of Open Access Journals (Sweden)

D. Lens

2013-03-01

Full Text Available In heavy-ion synchrotrons such as the SIS18 at Helmholtzzentrum für Schwerionenforschung, Helmholtz Centre for Heavy Ion Research (GSI, coherent oscillations of the particle bunches are damped by rf feedback systems to increase the stability and to improve the beam quality. In the longitudinal direction, important modes are the coherent longitudinal dipole and quadrupole oscillation. In this paper we present a new and rigorous approach to analyze the longitudinal feedback to damp these modes. The results are applied to the rf feedback loop at GSI that damps the quadrupole mode. The stability analysis is compared with simulations and is in good agreement with results of a beam experiment. Finally, we summarize practical implications for the operation of the feedback system regarding performance and stability.

General Output Feedback Stabilization for Fractional Order Systems: An LMI Approach

Directory of Open Access Journals (Sweden)

Yiheng Wei

2014-01-01

Full Text Available This paper is concerned with the problem of general output feedback stabilization for fractional order linear time-invariant (FO-LTI systems with the fractional commensurate order 0<α<2. The objective is to design suitable output feedback controllers that guarantee the stability of the resulting closed-loop systems. Based on the slack variable method and our previous stability criteria, some new results in the form of linear matrix inequality (LMI are developed to the static and dynamic output feedback controllers synthesis for the FO-LTI system with 0<α<1. Furthermore, the results are extended to stabilize the FO-LTI systems with 1≤α<2. Finally, robust output feedback control is discussed. Numerical examples are given to illustrate the effectiveness of the proposed design methods.

Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

Science.gov (United States)

Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui

2018-06-01

This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.

Studies of feedback stabilization of axisymmetric modes in deformable tokamak plasmas

International Nuclear Information System (INIS)

Ward, D.J.

1991-01-01

A new linear MHD stability code, NOVA-W, is described and applied to the study of the feedback stabilization of the axisymmetric mode in deformable tokamak plasma. The NOVA-W code is a modification of the non-variational MHD stability code NOVA that includes the effects of resistive passive conductors and active feedback circuits. The vacuum calculation has been reformulated in terms of the perturbed poloidal flux to allow the inclusion of perturbed toroidal currents outside the plasma. The boundary condition at the plasma-vacuum interface relates the instability displacement to the perturbed poloidal flux. This allows a solution of the linear MHD stability equations with the feedback effects included. The code has been tested for the case of passive stabilization against a simplified analytic model and against a different numerical calculation for a realistic tokamak configuration. The comparisons demonstrate the accuracy of the NOVA-W results. The NOVA-W code is used to examine the effects of plasma deformability on feedback stabilization. It is seen that plasmas with shaped cross sections have unstable motion different from a rigid shift. Plasma equilibria with large triangularity show particularly significant deviations from a uniform rigid shift. Furthermore, the placement of passive conductors is shown to modify the non-rigid components of the motion in a way that reduces the stabilizing effects of these conductors. The eigenfunction is also modified under the effects of active feedback. This deformation is seen to depend strongly on the position of the flux loops. These non-rigid components of the eigenfunction always serve to reduce the stabilizing effect of the active feedback system by reducing the measurable poloidal flux at the flux-loop locations

Adaptive nonlinear control using input normalized neural networks

International Nuclear Information System (INIS)

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

2008-01-01

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

Stability analysis of fractional-order Hopfield neural networks with time delays.

Science.gov (United States)

Wang, Hu; Yu, Yongguang; Wen, Guoguang

2014-07-01

This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

Science.gov (United States)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

International Nuclear Information System (INIS)

Vitela, J.E.; Martinell, J.J.

1998-01-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN. (author)

Synchronization of cellular neural networks of neutral type via dynamic feedback controller

International Nuclear Information System (INIS)

Park, Ju H.

2009-01-01

In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.

Trait self-esteem and neural activities related to self-evaluation and social feedback

Science.gov (United States)

Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

2016-01-01

Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one’s own personality traits and of others’ opinion about one’s own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one’s own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback. PMID:26842975

Trait self-esteem and neural activities related to self-evaluation and social feedback.

Science.gov (United States)

Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

2016-02-04

Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback.

UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR

Directory of Open Access Journals (Sweden)

S. S. Andropov

2016-09-01

Full Text Available A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

Directory of Open Access Journals (Sweden)

Miriam eZacksenhouse

2015-05-01

Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

Science.gov (United States)

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Neural networks for feedback feedforward nonlinear control systems.

Science.gov (United States)

Parisini, T; Zoppoli, R

1994-01-01

This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.

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

Science.gov (United States)

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

2016-04-01

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

Identification of neural structures involved in stuttering using vibrotactile feedback.

Science.gov (United States)

Cheadle, Oliver; Sorger, Clarissa; Howell, Peter

Feedback delivered over auditory and vibratory afferent pathways has different effects on the fluency of people who stutter (PWS). These features were exploited to investigate the neural structures involved in stuttering. The speech signal vibrated locations on the body (vibrotactile feedback, VTF). Eleven PWS read passages under VTF and control (no-VTF) conditions. All combinations of vibration amplitude, synchronous or delayed VTF and vibrator position (hand, sternum or forehead) were presented. Control conditions were performed at the beginning, middle and end of test sessions. Stuttering rate, but not speaking rate, differed between the control and VTF conditions. Notably, speaking rate did not change between when VTF was delayed versus when it was synchronous in contrast with what happens with auditory feedback. This showed that cerebellar mechanisms, which are affected when auditory feedback is delayed, were not implicated in the fluency-enhancing effects of VTF, suggesting that there is a second fluency-enhancing mechanism. Copyright © 2018 Elsevier Inc. All rights reserved.

Neural activations associated with feedback and retrieval success

Science.gov (United States)

Wiklund-Hörnqvist, Carola; Andersson, Micael; Jonsson, Bert; Nyberg, Lars

2017-11-01

There is substantial behavioral evidence for a phenomenon commonly called "the testing effect", i.e. superior memory performance after repeated testing compared to re-study of to-be-learned materials. However, considerably less is known about the underlying neuro-cognitive processes that are involved in the initial testing phase, and thus underlies the actual testing effect. Here, we investigated functional brain activity related to test-enhanced learning with feedback. Subjects learned foreign vocabulary across three consecutive tests with correct-answer feedback. Functional brain-activity responses were analyzed in relation to retrieval and feedback events, respectively. Results revealed up-regulated activity in fronto-striatal regions during the first successful retrieval, followed by a marked reduction in activity as a function of improved learning. Whereas feedback improved behavioral performance across consecutive tests, feedback had a negligable role after the first successful retrieval for functional brain-activity modulations. It is suggested that the beneficial effects of test-enhanced learning is regulated by feedback-induced updating of memory representations, mediated via the striatum, that might underlie the stabilization of memory commonly seen in behavioral studies of the testing effect.

Climate-carbon cycle feedbacks under stabilization: uncertainty and observational constraints

International Nuclear Information System (INIS)

Jones, Chris D.; Cox, Peter M.; Huntingford, Chris

2006-01-01

Avoiding 'dangerous climate change' by stabilization of atmospheric CO 2 concentrations at a desired level requires reducing the rate of anthropogenic carbon emissions so that they are balanced by uptake of carbon by the natural terrestrial and oceanic carbon cycles. Previous calculations of profiles of emissions which lead to stabilized CO 2 levels have assumed no impact of climate change on this natural carbon uptake. However, future climate change effects on the land carbon cycle are predicted to reduce its ability to act as a sink for anthropogenic carbon emissions and so quantification of this feedback is required to determine future permissible emissions. Here, we assess the impact of the climate-carbon cycle feedback and attempt to quantify its uncertainty due to both within-model parameter uncertainty and between-model structural uncertainty. We assess the use of observational constraints to reduce uncertainty in the future permissible emissions for climate stabilization and find that all realistic carbon cycle feedbacks consistent with the observational record give permissible emissions significantly less than previously assumed. However, the observational record proves to be insufficient to tightly constrain carbon cycle processes or future feedback strength with implications for climate-carbon cycle model evaluation

Stability analysis for cellular neural networks with variable delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result

Exponential stability of delayed fuzzy cellular neural networks with diffusion

International Nuclear Information System (INIS)

Huang Tingwen

2007-01-01

The exponential stability of delayed fuzzy cellular neural networks (FCNN) with diffusion is investigated. Exponential stability, significant for applications of neural networks, is obtained under conditions that are easily verified by a new approach. Earlier results on the exponential stability of FCNN with time-dependent delay, a special case of the model studied in this paper, are improved without using the time-varying term condition: dτ(t)/dt < μ

System and method for determining stability of a neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2011-01-01

Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

Calculations of axisymmetric stability of tokamak plasmas with active and passive feedback

International Nuclear Information System (INIS)

Ward, D.J.; Jardin, S.C.; Cheng, C.Z.

1991-07-01

A new linear MHD stability code, NOVA-W, has been developed in order to study feedback stabilization of the axisymmetric mode in deformable tokamak plasmas. The NOVA-W code is a modification of the non-variational MHD stability code NOVA that includes the effects of resistive passive conductors and active feedback circuits. The vacuum calculation has been reformulated in terms of the perturbed poloidal flux to allow the inclusion of perturbed toroidal currents outside the plasma. The boundary condition at the plasma-vacuum interface relates the instability displacement to the perturbed poloidal flux. This allows a solution of the linear MHD stability equations with the feedback effects included. The passive stability predictions of the code have been tested both against a simplified analytic model and against a different numerical calculation for a realistic tokamak configuration. The comparisons demonstrate the accuracy of the NOVA-W results. Active feedback calculations are performed for the CIT tokamak design demonstrating the effect of varying the position of the flux loops that provide the measurements of vertical displacement. The results compare well with those computed earlier using a less efficient nonlinear code. 37 refs., 13 figs

Global robust stability of delayed recurrent neural networks

International Nuclear Information System (INIS)

Cao Jinde; Huang Deshuang; Qu Yuzhong

2005-01-01

This paper is concerned with the global robust stability of a class of delayed interval recurrent neural networks which contain time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. A new sufficient condition is presented for the existence, uniqueness, and global robust stability of equilibria for interval neural networks with time delays by constructing Lyapunov functional and using matrix-norm inequality. An error is corrected in an earlier publication, and an example is given to show the effectiveness of the obtained results

Two-Layer Feedback Neural Networks with Associative Memories

International Nuclear Information System (INIS)

Gui-Kun, Wu; Hong, Zhao

2008-01-01

We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)

Feedback stabilization of the resistive shell mode in a tokamak fusion reactor

International Nuclear Information System (INIS)

Fitzpatrick, R.

1997-01-01

Stabilization of the 'resistive shell mode' is vital to the success of the 'advanced tokamak' concept. The most promising reactor relevant approach is to apply external feedback using, for instance, the previously proposed 'fake rotating shell' scheme [R. Fitzpatrick and T. H. Jensen, Phys. Plasmas 3, 2641 (1996)]. This scheme, like other simple feedback schemes, only works if the feedback controlled conductors are located inside the 'critical radius' at which a perfectly conducting shell is just able to stabilize the ideal external kink mode. In general, this is not possible in a reactor, since engineering constraints demand that any feedback controlled conductors be placed outside the neutron shielding blanket (i.e., relatively far from the edge of the plasma). It is demonstrated that the fake rotating shell feedback scheme can be modified so that it works even when the feedback controlled conductors are located well beyond the critical radius. The gain, bandwidth, current, and total power requirements of such a feedback system for a reactor sized plasma are estimated to be less than 100, a few Hz, a fews tens of kA, and a few MW, respectively. These requirements could easily be met using existing technology. It is concluded that feedback stabilization of the resistive shell mode is possible in a tokamak fusion reactor. copyright 1997 American Institute of Physics

Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary Systems

KAUST Repository

Ghallab, Ahmed G.

2017-10-19

Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true.

Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary Systems

KAUST Repository

Ghallab, Ahmed G.; Mabrok, Mohamed; Petersen, Ian R.

2017-01-01

Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true.

Normal mode approach to modelling of feedback stabilization of the resistive wall mode

International Nuclear Information System (INIS)

Chu, M.S.; Chance, M.S.; Okabayashi, M.; Glasser, A.H.

2003-01-01

Feedback stabilization of the resistive wall mode (RWM) of a plasma in a general feedback configuration is formulated in terms of the normal modes of the plasma-resistive wall system. The growth/damping rates and the eigenfunctions of the normal modes are determined by an extended energy principle for the plasma during its open (feedback) loop operation. A set of equations are derived for the time evolution of these normal modes with currents in the feedback coils. The dynamics of the feedback system is completed by the prescription of the feedback logic. The feasibility of the feedback is evaluated by using the Nyquist diagram method or by solving the characteristic equations. The elements of the characteristic equations are formed from the growth and damping rates of the normal modes, the sensor matrix of the perturbation fluxes detected by the sensor loops, the excitation matrix of the energy input to the normal modes by the external feedback coils, and the feedback logic. (The RWM is also predicted to be excited by an external error field to a large amplitude when it is close to marginal stability.) This formulation has been implemented numerically and applied to the DIII-D tokamak. It is found that feedback with poloidal sensors is much more effective than feedback with radial sensors. Using radial sensors, increasing the number of feedback coils from a central band on the outboard side to include an upper and a lower band can substantially increase the effectiveness of the feedback system. The strength of the RWM that can be stabilized is increased from γτ w = 1 to 30 (γ is the growth rate of the RWM in the absence of feedback and τ w is the resistive wall time constant) Using poloidal sensors, just one central band of feedback coils is sufficient for the stabilization of the RWM with γτ w = 30. (author)

Stabilization of Large Generalized Lotka-Volterra Foodwebs By Evolutionary Feedback

Science.gov (United States)

Ackland, G. J.; Gallagher, I. D.

2004-10-01

Conventional ecological models show that complexity destabilizes foodwebs, suggesting that foodwebs should have neither large numbers of species nor a large number of interactions. However, in nature the opposite appears to be the case. Here we show that if the interactions between species are allowed to evolve within a generalized Lotka-Volterra model such stabilizing feedbacks and weak interactions emerge automatically. Moreover, we show that trophic levels also emerge spontaneously from the evolutionary approach, and the efficiency of the unperturbed ecosystem increases with time. The key to stability in large foodwebs appears to arise not from complexity perse but from evolution at the level of the ecosystem which favors stabilizing (negative) feedbacks.

Feedback Linearized Aircraft Control Using Dynamic Cell Structure

Science.gov (United States)

Jorgensen, C. C.

1998-01-01

A Dynamic Cell Structure (DCS ) Neural Network was developed which learns a topology representing network (TRN) of F-15 aircraft aerodynamic stability and control derivatives. The network is combined with a feedback linearized tracking controller to produce a robust control architecture capable of handling multiple accident and off-nominal flight scenarios. This paper describes network and its performance for accident scenarios including differential stabilator lock, soft sensor failure, control, stability derivative variation, and turbulence.

Stability analysis for stochastic BAM nonlinear neural network with delays

Science.gov (United States)

Lv, Z. W.; Shu, H. S.; Wei, G. L.

2008-02-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria.

Stability analysis for stochastic BAM nonlinear neural network with delays

International Nuclear Information System (INIS)

Lv, Z W; Shu, H S; Wei, G L

2008-01-01

In this paper, stochastic bidirectional associative memory neural networks with constant or time-varying delays is considered. Based on a Lyapunov-Krasovskii functional and the stochastic stability analysis theory, we derive several sufficient conditions in order to guarantee the global asymptotically stable in the mean square. Our investigation shows that the stochastic bidirectional associative memory neural networks are globally asymptotically stable in the mean square if there are solutions to some linear matrix inequalities(LMIs). Hence, the global asymptotic stability of the stochastic bidirectional associative memory neural networks can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed global asymptotic stability criteria

Accelerator and feedback control simulation using neural networks

International Nuclear Information System (INIS)

Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.

1991-05-01

Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs

Periodicity and stability for variable-time impulsive neural networks.

Science.gov (United States)

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluating the negative or valuing the positive? Neural mechanisms supporting feedback-based learning across development.

Science.gov (United States)

van Duijvenvoorde, Anna C K; Zanolie, Kiki; Rombouts, Serge A R B; Raijmakers, Maartje E J; Crone, Eveline A

2008-09-17

How children learn from positive and negative performance feedback lies at the foundation of successful learning and is therefore of great importance for educational practice. In this study, we used functional magnetic resonance imaging (fMRI) to examine the neural developmental changes related to feedback-based learning when performing a rule search and application task. Behavioral results from three age groups (8-9, 11-13, and 18-25 years of age) demonstrated that, compared with adults, 8- to 9-year-old children performed disproportionally more inaccurately after receiving negative feedback relative to positive feedback. Additionally, imaging data pointed toward a qualitative difference in how children and adults use performance feedback. That is, dorsolateral prefrontal cortex and superior parietal cortex were more active after negative feedback for adults, but after positive feedback for children (8-9 years of age). For 11- to 13-year-olds, these regions did not show differential feedback sensitivity, suggesting that the transition occurs around this age. Pre-supplementary motor area/anterior cingulate cortex, in contrast, was more active after negative feedback in both 11- to 13-year-olds and adults, but not 8- to 9-year-olds. Together, the current data show that cognitive control areas are differentially engaged during feedback-based learning across development. Adults engage these regions after signals of response adjustment (i.e., negative feedback). Young children engage these regions after signals of response continuation (i.e., positive feedback). The neural activation patterns found in 11- to 13-year-olds indicate a transition around this age toward an increased influence of negative feedback on performance adjustment. This is the first developmental fMRI study to compare qualitative changes in brain activation during feedback learning across distinct stages of development.

Stability switches, oscillatory multistability, and spatio-temporal patterns of nonlinear oscillations in recurrently delay coupled neural networks.

Science.gov (United States)

Song, Yongli; Makarov, Valeri A; Velarde, Manuel G

2009-08-01

A model of time-delay recurrently coupled spatially segregated neural assemblies is here proposed. We show that it operates like some of the hierarchical architectures of the brain. Each assembly is a neural network with no delay in the local couplings between the units. The delay appears in the long range feedforward and feedback inter-assemblies communications. Bifurcation analysis of a simple four-units system in the autonomous case shows the richness of the dynamical behaviors in a biophysically plausible parameter region. We find oscillatory multistability, hysteresis, and stability switches of the rest state provoked by the time delay. Then we investigate the spatio-temporal patterns of bifurcating periodic solutions by using the symmetric local Hopf bifurcation theory of delay differential equations and derive the equation describing the flow on the center manifold that enables us determining the direction of Hopf bifurcations and stability of the bifurcating periodic orbits. We also discuss computational properties of the system due to the delay when an external drive of the network mimicks external sensory input.

On the roles of direct feedback and error field correction in stabilizing resistive-wall modes

International Nuclear Information System (INIS)

In, Y.; Bogatu, I.N.; Kim, J.S.; Garofalo, A.M.; Jackson, G.L.; La Haye, R.J.; Schaffer, M.J.; Strait, E.J.; Lanctot, M.J.; Reimerdes, H.; Marrelli, L.; Martin, P.; Okabayashi, M.

2010-01-01

Active feedback control in the DIII-D tokamak has fully stabilized the current-driven ideal kink resistive-wall mode (RWM). While complete stabilization is known to require both low frequency error field correction (EFC) and high frequency feedback, unambiguous identification has been made about the distinctive role of each in a fully feedback-stabilized discharge. Specifically, the role of direct RWM feedback, which nullifies the RWM perturbation in a time scale faster than the mode growth time, cannot be replaced by low frequency EFC, which minimizes the lack of axisymmetry of external magnetic fields. (letter)

Active feedback stabilization of axisymmetric modes in highly elongated tokamak plasmas

International Nuclear Information System (INIS)

Ward, D.J.; Hofmann, F.

1993-07-01

Active feedback stabilization of the vertical instability is studied for highly elongated tokamak plasmas (1≤κ≤3), and evaluated in particular for the TCV configuration. It is shown that the feedback can strongly affect the form of the eigenfunction for these highly elongated equilibria, and this can have detrimental effects on the ability of the feedback system to properly detect and stabilize the plasma. A calculation of the vertical displacement that uses poloidal flux measurements, poloidal magnetic field measurements, and corrections for the vessel eddy currents and active feedback currents was found to be effective even in the cases with the worst deformations of the eigenfunction. We also examine how these deformations affect differently shaped equilibria, and it is seen that the magnitude of the deformation of the eigenfunction is strongly function of the plasma elongation. (author) 15 figs., 13 refs

The importance of cutaneous feedback on neural activation during maximal voluntary contraction

NARCIS (Netherlands)

Cruz-Montecinos, Carlos; Maas, Huub; Pellegrin-Friedmann, Carla; Tapia, Claudio

2017-01-01

Purpose: The purpose of this study was to investigate the importance of cutaneous feedback on neural activation during maximal voluntary contraction (MVC) of the ankle plantar flexors. Methods: The effects of cutaneous plantar anaesthesia were assessed in 15 subjects and compared to 15 controls,

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

Science.gov (United States)

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

2010-09-01

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

Imaging stability in force-feedback high-speed atomic force microscopy

International Nuclear Information System (INIS)

Kim, Byung I.; Boehm, Ryan D.

2013-01-01

We studied the stability of force-feedback high-speed atomic force microscopy (HSAFM) by imaging soft, hard, and biological sample surfaces at various applied forces. The HSAFM images showed sudden topographic variations of streaky fringes with a negative applied force when collected on a soft hydrocarbon film grown on a grating sample, whereas they showed stable topographic features with positive applied forces. The instability of HSAFM images with the negative applied force was explained by the transition between contact and noncontact regimes in the force–distance curve. When the grating surface was cleaned, and thus hydrophilic by removing the hydrocarbon film, enhanced imaging stability was observed at both positive and negative applied forces. The higher adhesive interaction between the tip and the surface explains the improved imaging stability. The effects of imaging rate on the imaging stability were tested on an even softer adhesive Escherichia coli biofilm deposited onto the grating structure. The biofilm and planktonic cell structures in HSAFM images were reproducible within the force deviation less than ∼0.5 nN at the imaging rate up to 0.2 s per frame, suggesting that the force-feedback HSAFM was stable for various imaging speeds in imaging softer adhesive biological samples. - Highlights: ► We investigated the imaging stability of force-feedback HSAFM. ► Stable–unstable imaging transitions rely on applied force and sample hydrophilicity. ► The stable–unstable transitions are found to be independent of imaging rate

Static Voltage Stability Analysis by Using SVM and Neural Network

Directory of Open Access Journals (Sweden)

Mehdi Hajian

2013-01-01

Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.

Fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks.

Science.gov (United States)

Li, Hongfei; Li, Chuandong; Huang, Tingwen; Zhang, Wanli

2018-02-01

This article is concerned with the fixed-time stabilization for impulsive Cohen-Grossberg BAM neural networks via two different controllers. By using a novel constructive approach based on some comparison techniques for differential inequalities, an improvement theorem of fixed-time stability for impulsive dynamical systems is established. In addition, based on the fixed-time stability theorem of impulsive dynamical systems, two different control protocols are designed to ensure the fixed-time stabilization of impulsive Cohen-Grossberg BAM neural networks, which include and extend the earlier works. Finally, two simulations examples are provided to illustrate the validity of the proposed theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stability of Nonlinear Systems with Unknown Time-varying Feedback Delay

Science.gov (United States)

Chunodkar, Apurva A.; Akella, Maruthi R.

2013-12-01

This paper considers the problem of stabilizing a class of nonlinear systems with unknown bounded delayed feedback wherein the time-varying delay is 1) piecewise constant 2) continuous with a bounded rate. We also consider application of these results to the stabilization of rigid-body attitude dynamics. In the first case, the time-delay in feedback is modeled specifically as a switch among an arbitrarily large set of unknown constant values with a known strict upper bound. The feedback is a linear function of the delayed states. In the case of linear systems with switched delay feedback, a new sufficiency condition for average dwell time result is presented using a complete type Lyapunov-Krasovskii (L-K) functional approach. Further, the corresponding switched system with nonlinear perturbations is proven to be exponentially stable inside a well characterized region of attraction for an appropriately chosen average dwell time. In the second case, the concept of the complete type L-K functional is extended to a class of nonlinear time-delay systems with unknown time-varying time-delay. This extension ensures stability robustness to time-delay in the control design for all values of time-delay less than the known upper bound. Model-transformation is used in order to partition the nonlinear system into a nominal linear part that is exponentially stable with a bounded perturbation. We obtain sufficient conditions which ensure exponential stability inside a region of attraction estimate. A constructive method to evaluate the sufficient conditions is presented together with comparison with the corresponding constant and piecewise constant delay. Numerical simulations are performed to illustrate the theoretical results of this paper.

ℋ∞ constant gain state feedback stabilization of stochastic hybrid systems with Wiener process

Directory of Open Access Journals (Sweden)

E. K. Boukas

2004-01-01

Full Text Available This paper considers the stabilization problem of the class of continuous-time linear stochastic hybrid systems with Wiener process. The ℋ∞ state feedback stabilization problem is treated. A state feedback controller with constant gain that does not require access to the system mode is designed. LMI-based conditions are developed to design the state feedback controller with constant gain that stochastically stabilizes the studied class of systems and, at the same time, achieve the disturbance rejection of a desired level. The minimum disturbance rejection is also determined. Numerical examples are given to show the usefulness of the proposed results.

Stability of bumps in piecewise smooth neural fields with nonlinear adaptation

KAUST Repository

Kilpatrick, Zachary P.

2010-06-01

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

Pilot acute study of feedback-controlled retrograde peristalsis invoked by neural gastric electrical stimulation

International Nuclear Information System (INIS)

Aelen, P; Jurkov, A; Aulanier, A; Mintchev, M P

2009-01-01

Neural gastric electrical stimulation (NGES) is a new method for invoking gastric contractions under microprocessor control. However, optimization of this technique using feedback mechanisms to minimize power consumption and maximize effectiveness has been lacking. The present pilot study proposes a prototype feedback-controlled neural gastric electric stimulator for the treatment of obesity. Both force-based and inter-electrode impedance-based feedback neurostimulators were implemented and tested. Four mongrel dogs (2 M, 2 F, weight 14.9 ± 2.3 kg) underwent subserosal implantation of two-channel, 1 cm, bipolar electrode leads and two force transducers in the distal antrum. Two of the dogs were stimulated with a force feedback system utilizing the force transducers, and the other two animals were stimulated utilizing an inter-electrode impedance-based feedback system utilizing the proximal electrode leads. Both feedback systems were able to recognize erythromycin-driven contractions of the stomach and were capable of overriding them with NGES-invoked retrograde contractions which exceeded the magnitudes of the erythromycin-driven contractions by an average of 100.6 ± 33.5% in all animals. The NGES-invoked contractions blocked the erythromycin-driven contractions past the proximal electrode pair and induced temporary gastroparesis in the vicinity of the distal force transducer despite the continuing erythromycin infusion. The amplitudes of the erythromycin-invoked contractions in the vicinity of the proximal force transducer decreased abruptly by an average of 47.9 ± 6.3% in all four dogs after triggering-invoked retrograde contractions, regardless of the specific feedback-controlled mechanism. The proposed technique could be helpful for retaining food longer in the stomach, thus inducing early satiety and diminishing food intake

Finite-time output feedback stabilization of high-order uncertain nonlinear systems

Science.gov (United States)

Jiang, Meng-Meng; Xie, Xue-Jun; Zhang, Kemei

2018-06-01

This paper studies the problem of finite-time output feedback stabilization for a class of high-order nonlinear systems with the unknown output function and control coefficients. Under the weaker assumption that output function is only continuous, by using homogeneous domination method together with adding a power integrator method, introducing a new analysis method, the maximal open sector Ω of output function is given. As long as output function belongs to any closed sector included in Ω, an output feedback controller can be developed to guarantee global finite-time stability of the closed-loop system.

Exponential stability of neural networks with asymmetric connection weights

International Nuclear Information System (INIS)

Yang Jinxiang; Zhong Shouming

2007-01-01

This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory

Feedback stabilization of axisymmetric modes in tokamaks

International Nuclear Information System (INIS)

Jardin, S.C.; Larrabee, D.A.

1982-01-01

Noncircular tokamak plasmas can be unstable to ideal MHD axisymmetric instabilities. Passive conductors with finite resistivity will at best slow down these instabilities to the resistive (L/R) time of the conductors. An active feedback system far from the plasma which responds on this resistive time can stabilize the system provided its mutual inductance with the passive coils is small enough

Orbit stability and feedback control in synchrotron radiation rings

International Nuclear Information System (INIS)

Yu, L.H.

1989-01-01

Stability of the electron orbit is essential for the utilization of a low emittance storage ring as a high brightness radiation source. We discuss the development of the measurement and feedback control of the closed orbit, with emphasis on the activities as the National Synchrotron Light Source of BNL. We discuss the performance of the beam position detectors in use and under development: the PUE rf detector, split ion chamber detector, photo-emission detector, solid state detector, and the graphite detector. Depending on the specific experiments, different beamlines require different tolerances on the orbit motion. Corresponding to these different requirements, we discuss two approaches to closed orbit feedback: the global and local feedback systems. Then we describe a new scheme for the real time global feedback by implementing a feedback system based upon a harmonic analysis of both the orbit movements and the correction magnetic fields. 14 refs., 6 figs., 2 tabs

Synchronization of chaotic recurrent neural networks with time-varying delays using nonlinear feedback control

International Nuclear Information System (INIS)

Cui Baotong; Lou Xuyang

2009-01-01

In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme

Globally exponential stability of neural network with constant and variable delays

International Nuclear Information System (INIS)

Zhao Weirui; Zhang Huanshui

2006-01-01

This Letter presents new sufficient conditions of globally exponential stability of neural networks with delays. We show that these results generalize recently published globally exponential stability results. In particular, several different globally exponential stability conditions in the literatures which were proved using different Lyapunov functionals are generalized and unified by using the same Lyapunov functional and the technique of inequality of integral. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks. Those conditions are less restrictive than those given in the earlier references

On fully three-dimensional resistive wall mode and feedback stabilization computations

International Nuclear Information System (INIS)

Strumberger, E.; Merkel, P.; Sempf, M.; Guenter, S.

2008-01-01

Resistive walls, located close to the plasma boundary, reduce the growth rates of external kink modes to resistive time scales. For such slowly growing resistive wall modes, the stabilization by an active feedback system becomes feasible. The fully three-dimensional stability code STARWALL, and the feedback optimization code OPTIM have been developed [P. Merkel and M. Sempf, 21st IAEA Fusion Energy Conference 2006, Chengdu, China (International Atomic Energy Agency, Vienna, 2006, paper TH/P3-8] to compute the growth rates of resistive wall modes in the presence of nonaxisymmetric, multiply connected wall structures and to model the active feedback stabilization of these modes. In order to demonstrate the capabilities of the codes and to study the effect of the toroidal mode coupling caused by multiply connected wall structures, the codes are applied to test equilibria using the resistive wall structures currently under debate for ITER [M. Shimada et al., Nucl. Fusion 47, S1 (2007)] and ASDEX Upgrade [W. Koeppendoerfer et al., Proceedings of the 16th Symposium on Fusion Technology, London, 1990 (Elsevier, Amsterdam, 1991), Vol. 1, p. 208

Improving the Robustness of Deep Neural Networks via Stability Training

OpenAIRE

Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

2016-01-01

In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

Exponential stability of uncertain stochastic neural networks with mixed time-delays

International Nuclear Information System (INIS)

Wang Zidong; Lauria, Stanislao; Fang Jian'an; Liu Xiaohui

2007-01-01

This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria

Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks

International Nuclear Information System (INIS)

Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.

2012-01-01

This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.

The effects of plasma deformability on the feedback stabilization of axisymmetric modes in tokamak plasmas

International Nuclear Information System (INIS)

Ward, D.J.; Jardin, S.C.

1991-09-01

The effects of plasma deformability on the feedback stabilization of axisymmetric modes of tokamak plasmas are studied. It is seen that plasmas with strongly shaped cross sections have unstable motion different from a rigid shift. Furthermore, the placement of passive conductors is shown to modify the non-rigid components of the eigenfunction in a way that reduces the stabilizing eddy currents in these conductors. Passive feedback results using several equilibria of varying shape are presented. The eigenfunction is also modified under the effects of active feedback. This deformation is seen to depend strongly on the position of the flux loops which are used to determine plasma vertical position for the active feedback system. The variations of these non-rigid components of the eigenfunction always serve to reduce the stabilizing effect of the active feedback system by reducing the measurable poloidal flux at the flux-loop locations. Active feedback results are presented for the PBX-M tokamak configuration. (author) 19 figs., 2 tabs., 30 refs

Globally exponential stability condition of a class of neural networks with time-varying delays

International Nuclear Information System (INIS)

Liao, T.-L.; Yan, J.-J.; Cheng, C.-J.; Hwang, C.-C.

2005-01-01

In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein

Can vibratory feedback be used to improve postural stability in persons with transtibial limb loss?

Science.gov (United States)

Rusaw, David; Hagberg, Kerstin; Nolan, Lee; Ramstrand, Nerrolyn

2012-01-01

The use of vibration as a feedback modality to convey motion of the body has been shown to improve measures of postural stability in some groups of patients. Because individuals using transtibial prostheses lack sensation distal to the amputation, vibratory feedback could possibly be used to improve their postural stability. The current investigation provided transtibial prosthesis users (n = 24, mean age 48 yr) with vibratory feedback proportional to the signal received from force transducers located under the prosthetic foot. Postural stability was evaluated by measuring center of pressure (CoP) movement, limits of stability, and rhythmic weight shift while participants stood on a force platform capable of rotations in the pitch plane (toes up/toes down). The results showed that the vibratory feedback increased the mediolateral displacement amplitude of CoP in standing balance and reduced the response time to rapid voluntary movements of the center of gravity. The results suggest that the use of vibratory feedback in an experimental setting leads to improvements in fast open-loop mechanisms of postural control in transtibial prosthesis users.

Active feedback stabilization of the flute instability in a mirror machine using field-aligned coils

International Nuclear Information System (INIS)

Lifshitz, A.; Be'ery, I.; Fisher, A.; Ron, A.; Fruchtman, A.

2012-01-01

A plasma confined in linear mirror machines is unstable even at low β, mainly because of the flute instability. One possible way to stabilize the plasma is to use active feedback to correct the plasma shape in real time. The theoretically investigated apparatus consists of feedback coils aligned with the magnetic field, immersed in a cold plasma around the hot core. When the current through the feedback coils changes, the plasma moves to conserve the magnetic flux via compressional Alfvén waves. An analytical model is used to find a robust feedback algorithm with zero residual currents. It is shown that due to the plasma's rotation, maximal stability is obtained with a large phase angle between the perturbations' modes and the feedback integral-like term. Finally, a two-dimensional MHD simulation implementing the above algorithm in fact shows stabilization of the plasma with zero residual currents. (paper)

Stereotype threat engenders neural attentional bias toward negative feedback to undermine performance.

Science.gov (United States)

Forbes, Chad E; Leitner, Jordan B

2014-10-01

Stereotype threat, a situational pressure individuals experience when they fear confirming a negative group stereotype, engenders a cascade of physiological stress responses, negative appraisals, and performance monitoring processes that tax working memory resources necessary for optimal performance. Less is known, however, about how stereotype threat biases attentional processing in response to performance feedback, and how such attentional biases may undermine performance. Women received feedback on math problems in stereotype threatening compared to stereotype-neutral contexts while continuous EEG activity was recorded. Findings revealed that stereotype threatened women elicited larger midline P100 ERPs, increased phase locking between anterior cingulate cortex and dorsolateral prefrontal cortex (two regions integral for attentional processes), and increased power in left fusiform gyrus in response to negative feedback compared to positive feedback and women in stereotype-neutral contexts. Increased power in left fusiform gyrus in response to negative feedback predicted underperformance on the math task among stereotype threatened women only. Women in stereotype-neutral contexts exhibited the opposite trend. Findings suggest that in stereotype threatening contexts, neural networks integral for attention and working memory are biased toward negative, stereotype confirming feedback at very early speeds of information processing. This bias, in turn, plays a role in undermining performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural network based approach for tuning of SNS feedback and feedforward controllers

International Nuclear Information System (INIS)

Kwon, Sung-Il; Prokop, Mark S.; Regan, Amy H.

2002-01-01

The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.

Beam stability in synchrotrons with digital transverse feedback systems in dependence on beam tunes

International Nuclear Information System (INIS)

Zhabitskij, V.M.

2011-01-01

The beam stability problem in synchrotrons with a digital transverse feedback system (TFS) is studied. The TFS damper kicker (DK) corrects the transverse momentum of a bunch in proportion to its displacement from the closed orbit measured at the location of the beam position monitor (BPM). It is shown that the area and configuration of the beam stability separatrix depend on the beam tune, the feedback gain, the phase balance between the phase advance from BPM to DK and the phase response of the feedback chain at the betatron frequency

Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2009-01-01

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.

Rapid feedback control and stabilization of an optical tweezers with a budget microcontroller

International Nuclear Information System (INIS)

Nino, Daniel; Wang, Haowei; N Milstein, Joshua

2014-01-01

Laboratories ranging the scientific disciplines employ feedback control to regulate variables within their experiments, from the flow of liquids within a microfluidic device to the temperature within a cell incubator. We have built an inexpensive, yet fast and rapidly deployed, feedback control system that is straightforward and flexible to implement from a commercially available Arduino Due microcontroller. This is in comparison with the complex, time-consuming and often expensive electronics that are commonly implemented. As an example of its utility, we apply our feedback controller to the task of stabilizing the main trapping laser of an optical tweezers. The feedback controller, which is inexpensive yet fast and rapidly deployed, was implemented from hacking an open source Arduino Due microcontroller. Our microcontroller based feedback system can stabilize the laser intensity to a few tenths of a per cent at 200 kHz, which is an order of magnitude better than the laser's base specifications, illustrating the utility of these devices. (paper)

Rapid feedback control and stabilization of an optical tweezers with a budget microcontroller

Energy Technology Data Exchange (ETDEWEB)

Nino, Daniel; Wang, Haowei; N Milstein, Joshua, E-mail: josh.milstein@utoronto.ca [Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, ON L5L 1C6 (Canada)

2014-09-01

Laboratories ranging the scientific disciplines employ feedback control to regulate variables within their experiments, from the flow of liquids within a microfluidic device to the temperature within a cell incubator. We have built an inexpensive, yet fast and rapidly deployed, feedback control system that is straightforward and flexible to implement from a commercially available Arduino Due microcontroller. This is in comparison with the complex, time-consuming and often expensive electronics that are commonly implemented. As an example of its utility, we apply our feedback controller to the task of stabilizing the main trapping laser of an optical tweezers. The feedback controller, which is inexpensive yet fast and rapidly deployed, was implemented from hacking an open source Arduino Due microcontroller. Our microcontroller based feedback system can stabilize the laser intensity to a few tenths of a per cent at 200 kHz, which is an order of magnitude better than the laser's base specifications, illustrating the utility of these devices. (paper)

Global asymptotic stability of delayed Cohen-Grossberg neural networks

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

In this letter, the global asymptotic stability of a class of Cohen-Grossberg neural networks with time-varying delays is discussed. A new set of sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence. Our criteria represent an extension of the existing results in literatures. An example is also presented to compare our results with the previous results

The effect of normative feedback on stability and efficacy of some selected muscles in a balancing task

Directory of Open Access Journals (Sweden)

Saeed Ashrafpoor Navaee

2016-03-01

Full Text Available The purpose of the present study was to determine the effects of normative feedback on stability and efficacy of some selected muscles at different task difficulty levels in novice individuals. Thirty participants (age Mean= 22.60, SD=1.89 years were randomly assigned into three groups of positive, negative normative feedback and control.  The experimental groups participated in 160 acquisition trials (16 blocks of 10trials for 4 consecutive days (40 per day. Post test was performed after last practice session. The result of ANOVA-repeated measure test indicated that positive normative feedback group outperformed the other groups in stability indices of overall stability (P=0.004, anterior-posterior (P=0.01 and medial-lateral (P=0.001. In addition, the result of Covariance test at electromyography indices of the Soleus and Peroneus brevis showed significant differences in the favor of positive normative feedback in post-test. The findings of the present study showed that normative feedback has functional motivation affect that directly influences physiological changes level of stability control. KEY WORDS: Electromyography, knowledge of result, normative feedback, performance, stability control.

On global exponential stability of high-order neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Baoyong; Xu Shengyuan; Li Yongmin; Chu Yuming

2007-01-01

This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria

On global exponential stability of high-order neural networks with time-varying delays

Energy Technology Data Exchange (ETDEWEB)

Zhang Baoyong [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: baoyongzhang@yahoo.com.cn; Xu Shengyuan [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China)]. E-mail: syxu02@yahoo.com.cn; Li Yongmin [School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu (China) and Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)]. E-mail: ymlwww@163.com; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou 313000, Zhejiang (China)

2007-06-18

This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria.

Nonlinear power flow feedback control for improved stability and performance of airfoil sections

Science.gov (United States)

Wilson, David G.; Robinett, III, Rush D.

2013-09-03

A computer-implemented method of determining the pitch stability of an airfoil system, comprising using a computer to numerically integrate a differential equation of motion that includes terms describing PID controller action. In one model, the differential equation characterizes the time-dependent response of the airfoil's pitch angle, .alpha.. The computer model calculates limit-cycles of the model, which represent the stability boundaries of the airfoil system. Once the stability boundary is known, feedback control can be implemented, by using, for example, a PID controller to control a feedback actuator. The method allows the PID controller gain constants, K.sub.I, K.sub.p, and K.sub.d, to be optimized. This permits operation closer to the stability boundaries, while preventing the physical apparatus from unintentionally crossing the stability boundaries. Operating closer to the stability boundaries permits greater power efficiencies to be extracted from the airfoil system.

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

Science.gov (United States)

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

Global stability of stochastic high-order neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Wang Zidong; Fang Jianan; Liu Xiaohui

2008-01-01

High-order neural networks can be considered as an expansion of Hopfield neural networks, and have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks. In this paper, the global asymptotic stability analysis problem is considered for a class of stochastic high-order neural networks with discrete and distributed time-delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, several sufficient conditions are derived, which guarantee the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the stochastic high-order delayed neural networks under consideration are globally asymptotically stable in the mean square if two linear matrix inequalities (LMIs) are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also shown that the main results in this paper cover some recently published works. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria

Stability of Delayed Hopfield Neural Networks with Variable-Time Impulses

Directory of Open Access Journals (Sweden)

Yangjun Pei

2014-01-01

Full Text Available In this paper the globally exponential stability criteria of delayed Hopfield neural networks with variable-time impulses are established. The proposed criteria can also be applied in Hopfield neural networks with fixed-time impulses. A numerical example is presented to illustrate the effectiveness of our theoretical results.

Stability and synchronization control of stochastic neural networks

CERN Document Server

Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

2016-01-01

This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

Feedback stabilization of controlled dynamical systems in honor of Laurent Praly

CERN Document Server

2017-01-01

This book is a tribute to Professor Laurent Praly and follows on from a workshop celebrating the occasion of his 60th birthday. It presents new and unified visions of the numerous problems that Laurent Praly has worked on in his prolific career: adaptive control, output feedback and observers, stability and stabilization. His main contributions are the central topic of this book. The book collects contributions written by prominent international experts in the control community, addressing a rich variety of topics: emerging ideas, advanced applications, and theoretical concepts. Organized in three sections, the first section covers the field of adaptive control, where Laurent Praly started his career. The second section focuses on stabilization and output feedback, which is also the topic of the second half of his career. Lastly, the third section presents the emerging research that will form Laurent Praly’s scientific legacy.

Stability analysis of embedded nonlinear predictor neural generalized predictive controller

Directory of Open Access Journals (Sweden)

Hesham F. Abdel Ghaffar

2014-03-01

Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

Neural dynamics of feedforward and feedback processing in figure-ground segregation.

Science.gov (United States)

Layton, Oliver W; Mingolla, Ennio; Yazdanbakhsh, Arash

2014-01-01

Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure's interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.

Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation

Directory of Open Access Journals (Sweden)

Oliver W. Layton

2014-09-01

Full Text Available Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure’s interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells, and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells. Neurons (convex cells that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.

Neural dynamics of feedforward and feedback processing in figure-ground segregation

Science.gov (United States)

Layton, Oliver W.; Mingolla, Ennio; Yazdanbakhsh, Arash

2014-01-01

Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure's interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation. PMID:25346703

The pointer basis and the feedback stabilization of quantum systems

International Nuclear Information System (INIS)

Li, L; Chia, A; Wiseman, H M

2014-01-01

The dynamics for an open quantum system can be ‘unravelled’ in infinitely many ways, depending on how the environment is monitored, yielding different sorts of conditioned states, evolving stochastically. In the case of ideal monitoring these states are pure, and the set of states for a given monitoring forms a basis (which is overcomplete in general) for the system. It has been argued elsewhere (Atkins et al 2005 Europhys. Lett. 69 163) that the ‘pointer basis’ as introduced by Zurek et al (1993 Phys. Rev. Lett. 70 1187), should be identified with the unravelling-induced basis which decoheres most slowly. Here we show the applicability of this concept of pointer basis to the problem of state stabilization for quantum systems. In particular we prove that for linear Gaussian quantum systems, if the feedback control is assumed to be strong compared to the decoherence of the pointer basis, then the system can be stabilized in one of the pointer basis states with a fidelity close to one (the infidelity varies inversely with the control strength). Moreover, if the aim of the feedback is to maximize the fidelity of the unconditioned system state with a pure state that is one of its conditioned states, then the optimal unravelling for stabilizing the system in this way is that which induces the pointer basis for the conditioned states. We illustrate these results with a model system: quantum Brownian motion. We show that even if the feedback control strength is comparable to the decoherence, the optimal unravelling still induces a basis very close to the pointer basis. However if the feedback control is weak compared to the decoherence, this is not the case. (paper)

Some criteria for robust stability of Cohen-Grossberg neural networks with delays

International Nuclear Information System (INIS)

Xiong Weili; Xu Baoguo

2008-01-01

This paper considers the problem of robust stability of Cohen-Grossberg neural networks with time-varying delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some sufficient conditions are derived to ensure the global robust convergence of the equilibrium point. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Comparisons between our results and previous results admits our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks. Numerical examples are given to illustrate the effectiveness of our results

Global exponential stability for discrete-time neural networks with variable delays

International Nuclear Information System (INIS)

Chen Wuhua; Lu Xiaomei; Liang Dongying

2006-01-01

This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples

Stability analysis of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time varying delays

International Nuclear Information System (INIS)

Ali, M. Syed

2014-01-01

In this paper, the global asymptotic stability problem of Markovian jumping stochastic Cohen—Grossberg neural networks with discrete and distributed time-varying delays (MJSCGNNs) is considered. A novel LMI-based stability criterion is obtained by constructing a new Lyapunov functional to guarantee the asymptotic stability of MJSCGNNs. Our results can be easily verified and they are also less restrictive than previously known criteria and can be applied to Cohen—Grossberg neural networks, recurrent neural networks, and cellular neural networks. Finally, the proposed stability conditions are demonstrated with numerical examples

Beam stability in synchrotrons with digital filters in the feedback loop of a transverse damper

International Nuclear Information System (INIS)

Zhabitskij, V.M.

2009-01-01

The stability of an ion beam in synchrotrons with digital filters in the feedback loop of a transverse damper is treated. Solving the characteristic equation allows one to calculate the achievable damping rates as a function of instability growth rate, feedback gain and parameters of the signal processing. A transverse feedback system (TFS) is required in synchrotrons to stabilize the high intensity ion beams against transverse instabilities and to damp the beam injection errors. The TFS damper kicker (DK) corrects the transverse momentum of a bunch in proportion to its displacement from the closed orbit at the location of the beam position monitor (BPM). The digital signal processing unit in the feedback loop between BPM and DK ensures a condition to achieve optimal damping. Damping rates of the feedback systems with digital notch, Hilbert and all-pass filters are analyzed in comparison with those in an ideal feedback system

Stability of a neural network model with small-world connections

International Nuclear Information System (INIS)

Li Chunguang; Chen Guanrong

2003-01-01

Small-world networks are highly clustered networks with small distances among the nodes. There are many biological neural networks that present this kind of connection. There are no special weightings in the connections of most existing small-world network models. However, this kind of simply connected model cannot characterize biological neural networks, in which there are different weights in synaptic connections. In this paper, we present a neural network model with weighted small-world connections and further investigate the stability of this model

Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays

International Nuclear Information System (INIS)

Wu Wei; Cui Baotong; Huang Min

2007-01-01

Global asymptotic stability of Cohen-Grossberg neural networks with constant and variable delays is studied. Some sufficient conditions for the neural networks are proposed to guarantee the global asymptotic convergence by using different Lyapunov functionals. Our criteria represent an extension of the existing results in literatures. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed Cohen-Grossberg neural networks. Those conditions are less restrictive than those given in the earlier reference

Global robust exponential stability for interval neural networks with delay

International Nuclear Information System (INIS)

Cui Shihua; Zhao Tao; Guo Jie

2009-01-01

In this paper, new sufficient conditions for globally robust exponential stability of neural networks with either constant delays or time-varying delays are given. We show the sufficient conditions for the existence, uniqueness and global robust exponential stability of the equilibrium point by employing Lyapunov stability theory and linear matrix inequality (LMI) technique. Numerical examples are given to show the approval of our results.

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.

LHC beam stability and feedback control

International Nuclear Information System (INIS)

Steinhagen, Ralph

2007-01-01

This report presents the stability and the control of the Large Hadron Collider's (LHC) two beam orbits and their particle momenta using beam-based feedback systems. The aim of this report is to contribute to a safe and reliable LHC commissioning and machine operation. The first part of the analysis gives an estimate of the expected sources of orbit and energy perturbations that can be grouped into environmental sources, machine-inherent sources and machine element failures: the slowest perturbation due to ground motion, tides, temperature fluctuations of the tunnel and other environmental influences are described in this report by a propagation model that is both qualitatively and quantitatively supported by geophone and beam motion measurements at LEP and other CERN accelerators. The second part of this analysis deals with the control of the two LHC beams' orbit and energy through automated feedback systems. Based on the reading of the more than 1056 beam position monitors (BPMs) that are distributed over the machine, a central global feedback controller calculates new deflection strengths for the more than 1060 orbit corrector magnets (CODs) that are suitable to correct the orbit and momentum around their references. this report provides an analysis of the BPMs and CODs involved in the orbit and energy feedback. The BPMs are based on a wide-band time normaliser circuit that converts the transverse beam position reading of each individual particle bunch into two laser pulses that are separated by a time delay and transmitted through optical fibres to an acquisition card that converts the delay signals into a digital position. A simple error model has been tested and compared to the measurement accuracy of LHC type BPMs, obtained through beam-based measurements in the SPS. The average beam position is controlled through 1060 superconducting and individually powered corrector dipole magnets. The proposed correction in 'time-domain' consists of a proportional

Robust stability of bidirectional associative memory neural networks with time delays

Science.gov (United States)

Park, Ju H.

2006-01-01

Based on the Lyapunov Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms.

Robust stability of bidirectional associative memory neural networks with time delays

International Nuclear Information System (INIS)

Park, Ju H.

2006-01-01

Based on the Lyapunov-Krasovskii functionals combined with linear matrix inequality approach, a novel stability criterion is proposed for asymptotic stability of bidirectional associative memory neural networks with time delays. A novel delay-dependent stability criterion is given in terms of linear matrix inequalities, which can be solved easily by various optimization algorithms

Multi-stability and almost periodic solutions of a class of recurrent neural networks

International Nuclear Information System (INIS)

Liu Yiguang; You Zhisheng

2007-01-01

This paper studies multi-stability, existence of almost periodic solutions of a class of recurrent neural networks with bounded activation functions. After introducing a sufficient condition insuring multi-stability, many criteria guaranteeing existence of almost periodic solutions are derived using Mawhin's coincidence degree theory. All the criteria are constructed without assuming the activation functions are smooth, monotonic or Lipschitz continuous, and that the networks contains periodic variables (such as periodic coefficients, periodic inputs or periodic activation functions), so all criteria can be easily extended to fit many concrete forms of neural networks such as Hopfield neural networks, or cellular neural networks, etc. Finally, all kinds of simulations are employed to illustrate the criteria

Effect of intermittent feedback control on robustness of human-like postural control system

Science.gov (United States)

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

THE DETERMINATION OF A CRITICAL VALUE FOR DYNAMIC STABILITY OF SEMICONDUCTOR LASER DIODE WITH EXTERNAL OPTICAL FEEDBACK

Directory of Open Access Journals (Sweden)

Remzi YILDIRIM

1998-01-01

Full Text Available In this study, dynamic stability analysis of semiconductor laser diodes with external optical feedback has been realized. In the analysis the frequency response of the transfer function of laser diode H jw( , the transfer m function of laser diode with external optical feedback TF jw( , and optical feedback transfer function m K jw( obtained from small signal equations has been m accomplished using Nyquist stability analysis in complex domain. The effect of optical feedback on the stability of the system has been introduced and to bring the laser diode to stable condition the working critical boundary range of dampig frequency and reflection power constant (R has been determined. In the study the reflection power has been taken as ( .

Stochastic stability analysis for delayed neural networks of neutral type with Markovian jump parameters

International Nuclear Information System (INIS)

Lou Xuyang; Cui Baotong

2009-01-01

In this paper, the problem of stochastic stability for a class of delayed neural networks of neutral type with Markovian jump parameters is investigated. The jumping parameters are modelled as a continuous-time, discrete-state Markov process. A sufficient condition guaranteeing the stochastic stability of the equilibrium point is derived for the Markovian jumping delayed neural networks (MJDNNs) with neutral type. The stability criterion not only eliminates the differences between excitatory and inhibitory effects on the neural networks, but also can be conveniently checked. The sufficient condition obtained can be essentially solved in terms of linear matrix inequality. A numerical example is given to show the effectiveness of the obtained results.

Neural control of magnetic suspension systems

Science.gov (United States)

Gray, W. Steven

1993-01-01

The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

Real-time feedback for spatiotemporal field stabilization in MR systems.

Science.gov (United States)

Duerst, Yolanda; Wilm, Bertram J; Dietrich, Benjamin E; Vannesjo, S Johanna; Barmet, Christoph; Schmid, Thomas; Brunner, David O; Pruessmann, Klaas P

2015-02-01

MR imaging and spectroscopy require a highly stable, uniform background field. The field stability is typically limited by hardware imperfections, external perturbations, or field fluctuations of physiological origin. The purpose of the present work is to address these issues by introducing spatiotemporal field stabilization based on real-time sensing and feedback control. An array of NMR field probes is used to sense the field evolution in a whole-body MR system concurrently with regular system operation. The field observations serve as inputs to a proportional-integral controller that governs correction currents in gradient and higher-order shim coils such as to keep the field stable in a volume of interest. The feedback system was successfully set up, currently reaching a minimum latency of 20 ms. Its utility is first demonstrated by countering thermal field drift during an EPI protocol. It is then used to address respiratory field fluctuations in a T2 *-weighted brain exam, resulting in substantially improved image quality. Feedback field control is an effective means of eliminating dynamic field distortions in MR systems. Third-order spatial control at an update time of 100 ms has proven sufficient to largely eliminate thermal and breathing effects in brain imaging at 7 Tesla. © 2014 Wiley Periodicals, Inc.

Stability and Bifurcation in Magnetic Flux Feedback Maglev Control System

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Wen-Qing Zhang

2013-01-01

Full Text Available Nonlinear properties of magnetic flux feedback control system have been investigated mainly in this paper. We analyzed the influence of magnetic flux feedback control system on control property by time delay and interfering signal of acceleration. First of all, we have established maglev nonlinear model based on magnetic flux feedback and then discussed hopf bifurcation’s condition caused by the acceleration’s time delay. The critical value of delayed time is obtained. It is proved that the period solution exists in maglev control system and the stable condition has been got. We obtained the characteristic values by employing center manifold reduction theory and normal form method, which represent separately the direction of hopf bifurcation, the stability of the period solution, and the period of the period motion. Subsequently, we discussed the influence maglev system on stability of by acceleration’s interfering signal and obtained the stable domain of interfering signal. Some experiments have been done on CMS04 maglev vehicle of National University of Defense Technology (NUDT in Tangshan city. The results of experiments demonstrate that viewpoints of this paper are correct and scientific. When time lag reaches the critical value, maglev system will produce a supercritical hopf bifurcation which may cause unstable period motion.

Global exponential stability of mixed discrete and distributively delayed cellular neural network

International Nuclear Information System (INIS)

Yao Hong-Xing; Zhou Jia-Yan

2011-01-01

This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov—Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result. (general)

Feedback stabilization of the axisymmetric instability of a deformable tokamak plasma

International Nuclear Information System (INIS)

Pomphrey, N.; Jardin, S.C.; Ward, D.J.

1989-01-01

The paper presents an analysis of the magnetohydrodynamic stability of the axisymmetric system consisting of a free boundary tokamak plasma with non-circular cross-section, finite resistivity passive conductors, and an active feedback system with magnetic flux pickup loops, a proportional amplifier with gain G and current carrying poloidal field coils. A numerical simulation of the system when G is set to zero identifies flux loop locations which correctly sense the plasma motion. However, when certain of these locations are incorporated into an active feedback scheme, the plasma fails to be stabilized, no matter what value of the gain is chosen. Analysis on the basis of an extended energy principle indicates that this failure is due to the deformability of the plasma cross-section. (author). 14 refs, 7 figs

Standard representation and unified stability analysis for dynamic artificial neural network models.

Science.gov (United States)

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.

Science.gov (United States)

Zhang, Wei; Huang, Tingwen; He, Xing; Li, Chuandong

2017-11-01

In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tracking performance and global stability guaranteed neural control of uncertain hypersonic flight vehicle

Directory of Open Access Journals (Sweden)

Tao Teng

2016-02-01

Full Text Available In this article, a global adaptive neural dynamic surface control with predefined tracking performance is developed for a class of hypersonic flight vehicles, whose accurate dynamics is hard to obtain. The control scheme developed in this paper overcomes the limitations of neural approximation region by employing a switching mechanism which incorporates an additional robust controller outside the neural approximation region to pull the transient state variables back when they overstep the neural approximation region, such that globally uniformly ultimately bounded stability can be guaranteed. Especially, the developed global adaptive neural control also improves the tracking performance by introducing an error transformation mechanism, such that both transient and steady-state performance can be shaped according to the predefined bounds. Simulation studies on the hypersonic flight vehicle validate that the designed controller has good velocity modulation and velocity stability performance.

Final analysis of the engineering data on the scyllac feedback stabilization experiment

International Nuclear Information System (INIS)

Kutac, K.J.; Kewish, R.W.; Miller, G.; Gribble, R.F.

1977-01-01

The feedback stabilization system consists of four basic components: plasma position detectors, a signal processor or mode analyzer driven by the position detector signals, power amplifiers which are driven by the mode analyzer, and feedback load coils driven by the power amplifiers. A short description of each of the four components of the system is presented. The location of the components in the experiment is shown

Global stability of discrete-time recurrent neural networks with impulse effects

International Nuclear Information System (INIS)

Zhou, L; Li, C; Wan, J

2008-01-01

This paper formulates and studies a class of discrete-time recurrent neural networks with impulse effects. A stability criterion, which characterizes the effects of impulse and stability property of the corresponding impulse-free networks on the stability of the impulsive networks in an aggregate form, is established. Two simplified and numerically tractable criteria are also provided

Decentralized Feedback Controllers for Exponential Stabilization of Hybrid Periodic Orbits: Application to Robotic Walking*

Science.gov (United States)

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially stabilize periodic orbits for a class of hybrid dynamical systems arising from bipedal walking. The algorithm assumes a class of parameterized and nonlinear decentralized feedback controllers which coordinate lower-dimensional hybrid subsystems based on a common phasing variable. The exponential stabilization problem is translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities, which can be easily solved with available software packages. A set of sufficient conditions for the convergence of the iterative algorithm to a stabilizing decentralized feedback control solution is presented. The power of the algorithm is demonstrated by designing a set of local nonlinear controllers that cooperatively produce stable walking for a 3D autonomous biped with 9 degrees of freedom, 3 degrees of underactuation, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:27990059

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

Science.gov (United States)

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

2014-06-01

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

A new delay-independent condition for global robust stability of neural networks with time delays.

Science.gov (United States)

Samli, Ruya

2015-06-01

This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bounded. By employing the results of Lyapunov stability theory and matrix theory, new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for delayed neural networks are presented. The results reported in this paper can be easily tested by checking some special properties of symmetric matrices associated with the parameter uncertainties of neural networks. We also present a numerical example to show the effectiveness of the proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improve beam position stability of SSRF BL15U beamline by using beam intensity feedback

International Nuclear Information System (INIS)

Li Guoqiang; Liang Dongxu; Yan Fen; Li Aiguo; Yu Xiaohan

2013-01-01

Background: The shaking of micro-focus spot in the vertical direction is found during the energy scan experiments, such as XAFS scan. The beam position of vertical direction changes obviously with the energy. Purpose: In order to make the beam position shaking amplitude less than 1/10 of the beam size. Methods: The beam position stability of SSRF BL15U beamline is improved by using beam intensity feedback. The feedback system include beam intensity monitor of the beamline and fine adjust mechanism of pitch 2 (the pitch angle of the second crystal of the double crystal monochromator). The feedback control of the beam position is realized by adjusting the pitch 2 to fix beam intensity at its maximum value. Results: The test results show that the vertical beam vibration below 10 Hz frequency is significantly reduced and also the beam position stability during photon energy scan is improved by more than 5 times. Conclusions: By adopting the new feedback systems, the stability of the beam spot on the specimen stage was dramatically improved which achieved the anticipated target. (authors)

Partial state feedback control of chaotic neural network and its application

International Nuclear Information System (INIS)

He Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

2007-01-01

The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition

Output feedback control of a quadrotor UAV using neural networks.

Science.gov (United States)

Dierks, Travis; Jagannathan, Sarangapani

2010-01-01

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result

Mean square exponential stability of stochastic delayed Hopfield neural networks

International Nuclear Information System (INIS)

Wan Li; Sun Jianhua

2005-01-01

Stochastic effects to the stability property of Hopfield neural networks (HNN) with discrete and continuously distributed delay are considered. By using the method of variation parameter, inequality technique and stochastic analysis, the sufficient conditions to guarantee the mean square exponential stability of an equilibrium solution are given. Two examples are also given to demonstrate our results

Global exponential stability for nonautonomous cellular neural networks with delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2006-01-01

In this Letter, by utilizing Lyapunov functional method and Halanay inequalities, we analyze global exponential stability of nonautonomous cellular neural networks with delay. Several new sufficient conditions ensuring global exponential stability of the network are obtained. The results given here extend and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results

A switched state feedback law for the stabilization of LTI systems.

Energy Technology Data Exchange (ETDEWEB)

Santarelli, Keith R.

2009-09-01

Inspired by prior work in the design of switched feedback controllers for second order systems, we develop a switched state feedback control law for the stabilization of LTI systems of arbitrary dimension. The control law operates by switching between two static gain vectors in such a way that the state trajectory is driven onto a stable n - 1 dimensional hyperplane (where n represents the system dimension). We begin by briefly examining relevant geometric properties of the phase portraits in the case of two-dimensional systems to develop intuition, and we then show how these geometric properties can be expressed as algebraic constraints on the switched vector fields that are applicable to LTI systems of arbitrary dimension. We then derive necessary and sufficient conditions to ensure stabilizability of the resulting switched system (characterized primarily by simple conditions on eigenvalues), and describe an explicit procedure for designing stabilizing controllers. We then show how the newly developed control law can be applied to the problem of minimizing the maximal Lyapunov exponent of the corresponding closed-loop state trajectories, and we illustrate the closed-loop transient performance of these switched state feedback controllers via multiple examples.

LHC beam stability and feedback control

Energy Technology Data Exchange (ETDEWEB)

Steinhagen, Ralph

2007-07-20

This report presents the stability and the control of the Large Hadron Collider's (LHC) two beam orbits and their particle momenta using beam-based feedback systems. The aim of this report is to contribute to a safe and reliable LHC commissioning and machine operation. The first part of the analysis gives an estimate of the expected sources of orbit and energy perturbations that can be grouped into environmental sources, machine-inherent sources and machine element failures: the slowest perturbation due to ground motion, tides, temperature fluctuations of the tunnel and other environmental influences are described in this report by a propagation model that is both qualitatively and quantitatively supported by geophone and beam motion measurements at LEP and other CERN accelerators. The second part of this analysis deals with the control of the two LHC beams' orbit and energy through automated feedback systems. Based on the reading of the more than 1056 beam position monitors (BPMs) that are distributed over the machine, a central global feedback controller calculates new deflection strengths for the more than 1060 orbit corrector magnets (CODs) that are suitable to correct the orbit and momentum around their references. this report provides an analysis of the BPMs and CODs involved in the orbit and energy feedback. The BPMs are based on a wide-band time normaliser circuit that converts the transverse beam position reading of each individual particle bunch into two laser pulses that are separated by a time delay and transmitted through optical fibres to an acquisition card that converts the delay signals into a digital position. A simple error model has been tested and compared to the measurement accuracy of LHC type BPMs, obtained through beam-based measurements in the SPS. The average beam position is controlled through 1060 superconducting and individually powered corrector dipole magnets. The proposed correction in 'time-domain' consists of a

Deterministic creation and stabilization of entanglement in circuit QED by homodyne-mediated feedback control

International Nuclear Information System (INIS)

Liu Zhuo; Kuang Luelin; Hu Kai; Xu Luting; Wei Suhua; Guo Lingzhen; Li Xinqi

2010-01-01

In a solid-state circuit QED system, we demonstrate that a homodyne-current-based feedback can create and stabilize highly entangled two-qubit states in the presence of a moderate noisy environment. Particularly, we present an extended analysis for the current-based Markovian feedback, which leads to an improved feedback scheme. We show that this is essential to achieve a desirable control effect by the use of dispersive measurement.

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.

ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

K G Nandha Kumar

2017-07-01

Full Text Available An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users’ satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users’ satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users’ satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.

Feedback stabilization of an l = 0, 1, 2 high-beta stellarator

International Nuclear Information System (INIS)

Bartsch, R.R.; Cantrell, E.L.; Gribble, R.F.; Klare, K.A.; Kutac, K.J.; Miller, G.; Quinn, W.E.

1978-05-01

Feedback stabilization of the Scyllac 120 0 toroidal sector is reported. The confinement time was increased by 10-20 μs using feedback to a maximum time of 35-45 μs, which is over 10 growth times of the long-wavelength m = 1 instability. These results were obtained after circuits providing flexible waveforms were used to drive auxiliary equilibrium windings. The resultant improved equilibrium agrees well with recent theory. It was observed that normally stable short-wavelength m = 1 modes could be driven unstable by feedback. This instability, caused by local feedback control, increases the feedback system energy consumption. An instability involving direct coupling of the feedback l = 2 field to the plasma l = 1 motion was also observed. The plasma parameters were: temperature, T/sub e/ approximately equal to T 1 approximately equal to 100 eV; density, n/sub e/ approximately equal to 2 x 10 16 cm -8 ; radius, a approximately equal to 1 cm; and β approximately equal to 0.7. Beta decreased significantly in 40 μs, which can be accounted for by classical resistivity and particle loss from the sector ends

Feedback and rotational stabilization of resistive wall modes in ITER

International Nuclear Information System (INIS)

Liu Yueqiang; Bondeson, A.; Chu, M.S.; La Haye, R.J.; Favez, J.-Y.; Lister, J.B.; Gribov, Y.; Gryaznevich, M.; Hender, T.C.; Howell, D.F.

2005-01-01

Different models have been introduced in the stability code MARS-F in order to study the damping effect of resistive wall modes (RWM) in rotating plasmas. Benchmark of MARS-F calculations with RWM experiments on JET and D3D indicates that the semi-kinetic damping model is a good candidate for explaining the damping mechanisms. Based on these results, the critical rotation speeds required for RWM stabilization in an advanced ITER scenario are predicted. Active feedback control of the n = 1 RWM in ITER is also studied using the MARS-F code. (author)

Stabilization of exact nonlinear Timoshenko beams in space by boundary feedback

Science.gov (United States)

Do, K. D.

2018-05-01

Boundary feedback controllers are designed to stabilize Timoshenko beams with large translational and rotational motions in space under external disturbances. The exact nonlinear partial differential equations governing motion of the beams are derived and used in the control design. The designed controllers guarantee globally practically asymptotically (and locally practically exponentially) stability of the beam motions at the reference state. The control design, well-posedness and stability analysis are based on various relationships between the earth-fixed and body-fixed coordinates, Sobolev embeddings, and a Lyapunov-type theorem developed to study well-posedness and stability for a class of evolution systems in Hilbert space. Simulation results are included to illustrate the effectiveness of the proposed control design.

Global robust stability of bidirectional associative memory neural networks with multiple time delays.

Science.gov (United States)

Senan, Sibel; Arik, Sabri

2007-10-01

This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

Novel global robust stability criterion for neural networks with delay

International Nuclear Information System (INIS)

Singh, Vimal

2009-01-01

A novel criterion for the global robust stability of Hopfield-type interval neural networks with delay is presented. An example illustrating the improvement of the present criterion over several recently reported criteria is given.

Stability Analysis and Application for Delayed Neural Networks Driven by Fractional Brownian Noise.

Science.gov (United States)

Zhou, Wuneng; Zhou, Xianghui; Yang, Jun; Zhou, Jun; Tong, Dongbing

2018-05-01

This paper deals with two types of the stability problem for the delayed neural networks driven by fractional Brownian noise (FBN). The existence and the uniqueness of the solution to the main system with respect to FBN are proved via fixed point theory. Based on Hilbert-Schmidt operator theory and analytic semigroup principle, the mild solution of the stochastic neural networks is obtained. By applying the stochastic analytic technique and some well-known inequalities, the asymptotic stability criteria and the exponential stability condition are established. Both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques.

Theoretical modeling of the feedback stabilization of external MHD modes of toroidal geometry

International Nuclear Information System (INIS)

Chance, M.S.; Chu, M.S.; Okabayashi, M.

2001-01-01

A theoretical framework for understanding the feedback mechanism against external MHD modes has been formulated. Efficient computational tools - the GATO stability code coupled with a substantially modified VACUUM code - have been developed to effectively design viable feedback systems against these modes. The analysis assumed a thin resistive shell and a feedback coil structure accurately modeled in θ, with only a single harmonic variation in φ. An optimized configuration and placement of the feedback and sensor coils as well as the time constants and induced currents in the enclosing resistive shell have been computed for the DIII-D device. Up to 90% of the effectiveness of an ideal wall can be achieved. (author)

Delay-dependent exponential stability of cellular neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Qiang; Wei Xiaopeng; Xu Jin

2005-01-01

The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Science.gov (United States)

Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

Time-dependent simulations of feedback stabilization of neoclassical tearing modes in KSTAR plasmas

Energy Technology Data Exchange (ETDEWEB)

Kim, Kyungjin [Department of Nuclear Engineering, Seoul National University, Seoul (Korea, Republic of); Na, Yong-Su, E-mail: ysna@snu.ac.kr [Department of Nuclear Engineering, Seoul National University, Seoul (Korea, Republic of); Kim, Hyun-Seok [Department of Nuclear Engineering, Seoul National University, Seoul (Korea, Republic of); Maraschek, M. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching bei München (Germany); Park, Y.S. [Department of Applied Physics and Applied Mathematics, Columbia University, New York (United States); Stober, J. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching bei München (Germany); Terzolo, L. [National Fusion Research Institute, Daejeon (Korea, Republic of); Zohm, H. [Max-Planck-Institut fuer Plasmaphysik, EURATOM Association, Garching bei München (Germany)

2014-06-15

A simulation is performed for feedback stabilization of neoclassical tearing mode (NTM) by electron cyclotron current drive (ECCD) for KSTAR in preparation for experiments. An integrated numerical system is constructed by coupling plasma transport, NTM stability, and heating and current drive modules and applied to a KSTAR plasma by assuming similar experimental conditions as ASDEX Upgrade to predict NTM behaviors in KSTAR. System identification is made with database produced by predictive simulations with this integrated numerical system so that three plasma response models are extracted which describe the relation between the EC poloidal launcher angle and the island width in KSTAR. Among them, the P1DI model exhibiting the highest fit accuracy is selected for designing a feedback controller based on the classical Proportional–Integral–Derivative (PID) concept. The controller is coupled with the integrated numerical system and applied to a simulation of NTM stabilization. It is observed that the controller can search and fully stabilize the mode even though the poloidal launch angle is misaligned with the island initially.

Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Space-time adaptive decision feedback neural receivers with data selection for high-data-rate users in DS-CDMA systems.

Science.gov (United States)

de Lamare, Rodrigo C; Sampaio-Neto, Raimundo

2008-11-01

A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.

New exponential stability criteria for stochastic BAM neural networks with impulses

International Nuclear Information System (INIS)

Sakthivel, R; Samidurai, R; Anthoni, S M

2010-01-01

In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Ito differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature.

New exponential stability criteria for stochastic BAM neural networks with impulses

Science.gov (United States)

Sakthivel, R.; Samidurai, R.; Anthoni, S. M.

2010-10-01

In this paper, we study the global exponential stability of time-delayed stochastic bidirectional associative memory neural networks with impulses and Markovian jumping parameters. A generalized activation function is considered, and traditional assumptions on the boundedness, monotony and differentiability of activation functions are removed. We obtain a new set of sufficient conditions in terms of linear matrix inequalities, which ensures the global exponential stability of the unique equilibrium point for stochastic BAM neural networks with impulses. The Lyapunov function method with the Itô differential rule is employed for achieving the required result. Moreover, a numerical example is provided to show that the proposed result improves the allowable upper bound of delays over some existing results in the literature.

Dynamic stability analysis of fractional order leaky integrator echo state neural networks

Science.gov (United States)

Pahnehkolaei, Seyed Mehdi Abedi; Alfi, Alireza; Tenreiro Machado, J. A.

2017-06-01

The Leaky integrator echo state neural network (Leaky-ESN) is an improved model of the recurrent neural network (RNN) and adopts an interconnected recurrent grid of processing neurons. This paper presents a new proof for the convergence of a Lyapunov candidate function to zero when time tends to infinity by means of the Caputo fractional derivative with order lying in the range (0, 1). The stability of Fractional-Order Leaky-ESN (FO Leaky-ESN) is then analyzed, and the existence, uniqueness and stability of the equilibrium point are provided. A numerical example demonstrates the feasibility of the proposed method.

Output Feedback Stabilization with Nonlinear Predictive Control: Asymptotic properties

Directory of Open Access Journals (Sweden)

Lars Imsland

2003-07-01

Full Text Available State space based nonlinear model predictive control (NM PC needs the state for the prediction of the system behaviour. Unfortunately, for most applications, not all states are directly measurable. To recover the unmeasured states, typically a stable state observer is used. However, this implies that the stability of the closed-loop should be examined carefully, since no general nonlinear separation principle exists. Recently semi-global practical stability results for output feedback NMPC using a high-gain observer for state estimation have been established. One drawback of this result is that (in general the observer gain must be increased, if the desired set the state should converge to is made smaller. We show that under slightly stronger assumptions, not only practical stability, but also convergence of the system states and observer error to the origin for a sufficiently large but bounded observer gain can be achieved.

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

Science.gov (United States)

Li, Yongming; Tong, Shaocheng

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

Nonlinear Feedback Control and Stability Analysis of a Proof-of-Work Blockchain

Directory of Open Access Journals (Sweden)

Geir Hovland

2017-10-01

Full Text Available In this paper a novel feedback controller and stability analysis of a blockchain implementation is developed by using a control engineering perspective. The controller output equals the difficulty adjustment in the mining process while the feedback variable is the average block time over a certain time period. The computational power (hash rate of the miners is considered a disturbance in the model. The developed controller is tested against a simulation model with constant disturbance, step and ramp responses as well as with a high-frequency sinusoidal disturbance. Stability and a fast response is demonstrated in all these cases with a controller which adjusts it's output at every new block. Finally the performance of the controller is implemented and demonstrated on a testnet with a constant hash rate as well as on the mainnet of a public open source blockchain project.

Thermal stability analysis and auxiliary power feedback control for the tokamak engineering test breeder (TETB-II)

International Nuclear Information System (INIS)

Sheng Guangzhao

1993-01-01

The thermal stability of TETB-II is analyzed using different methods, viz., POPCON, linear stability analysis and the time evolution calculation of plasma parameters. A thermal instability of the TETB-II is predicted. Auxiliary power feedback control for thermal stability appears feasible and efficient

Exponential stability of delayed recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Zidong; Liu Yurong; Yu Li; Liu Xiaohui

2006-01-01

In this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions

Stability results for stochastic delayed recurrent neural networks with discrete and distributed delays

Science.gov (United States)

Chen, Guiling; Li, Dingshi; Shi, Lin; van Gaans, Onno; Verduyn Lunel, Sjoerd

2018-03-01

We present new conditions for asymptotic stability and exponential stability of a class of stochastic recurrent neural networks with discrete and distributed time varying delays. Our approach is based on the method using fixed point theory, which do not resort to any Liapunov function or Liapunov functional. Our results neither require the boundedness, monotonicity and differentiability of the activation functions nor differentiability of the time varying delays. In particular, a class of neural networks without stochastic perturbations is also considered. Examples are given to illustrate our main results.

Global asymptotic stability analysis of bidirectional associative memory neural networks with time delays.

Science.gov (United States)

Arik, Sabri

2005-05-01

This paper presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all continuous nonmonotonic neuron activation functions. It is shown that in some special cases of the results, the stability criteria can be easily checked. Some examples are also given to compare the results with the previous results derived in the literature.

Delay-slope-dependent stability results of recurrent neural networks.

Science.gov (United States)

Li, Tao; Zheng, Wei Xing; Lin, Chong

2011-12-01

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov-Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.

Global Asymptotic Stability of Switched Neural Networks with Delays

Directory of Open Access Journals (Sweden)

Zhenyu Lu

2015-01-01

Full Text Available This paper investigates the global asymptotic stability of a class of switched neural networks with delays. Several new criteria ensuring global asymptotic stability in terms of linear matrix inequalities (LMIs are obtained via Lyapunov-Krasovskii functional. And here, we adopt the quadratic convex approach, which is different from the linear and reciprocal convex combinations that are extensively used in recent literature. In addition, the proposed results here are very easy to be verified and complemented. Finally, a numerical example is provided to illustrate the effectiveness of the results.

Pseudodifferential Perturbations and Stabilization of Distributed Parameter Systems: Dirichlet Feedback Control Problems

DEFF Research Database (Denmark)

Pedersen, Michael

1991-01-01

The stabilization problems for parabolic and hyperbolic partial differential equations with Dirichlet boundary condition are considered. The systems are stabilized by a boundary feedback in(1) The operator equation,(2) The boundary condition,(3) Both the operator equation and the boundary condition...... turns out to be a shortcut to some of the stabilization results of Lasiecka and Triggiani in [J. Differential Equations, 47 (1983), pp. 245-272], [SIAM J. Control Optim., 21(1983), pp. 766-802], and [Appl. Math. Optim., 8(1981), pp. 1-37], and it illuminates to some extent how a change of boundary...

THEORETICAL MODELING OF THE FEEDBACK STABILIZATION OF EXTERNAL MHD MODES IN TOROIDAL GEOMETRY

International Nuclear Information System (INIS)

CHANCE, M.S.; CHU, M.S.; OKABAYASHI, M.; TURNBULL, A.D.

2001-02-01

OAK-B135 A theoretical framework for understanding the feedback mechanism against external MHD modes has been formulated. Efficient computational tools--the GATO stability code coupled with a substantially modified VACUUM code--have been developed to effectively design viable feedback systems against these modes. The analysis assumed a thin resistive shell and a feedback coil structure accurately modeled in θ, with only a single harmonic variation in φ. Time constants and induced currents in the enclosing resistive shell are calculated. An optimized configuration based on an idealized model have been computed for the DIII-D device. Up to 90% of the effectiveness of an ideal wall can be achieved

Global asymptotic stability to a generalized Cohen-Grossberg BAM neural networks of neutral type delays.

Science.gov (United States)

Zhang, Zhengqiu; Liu, Wenbin; Zhou, Dongming

2012-01-01

In this paper, we first discuss the existence of a unique equilibrium point of a generalized Cohen-Grossberg BAM neural networks of neutral type delays by means of the Homeomorphism theory and inequality technique. Then, by applying the existence result of an equilibrium point and constructing a Lyapunov functional, we study the global asymptotic stability of the equilibrium solution to the above Cohen-Grossberg BAM neural networks of neutral type. In our results, the hypothesis for boundedness in the existing paper, which discussed Cohen-Grossberg neural networks of neutral type on the activation functions, are removed. Finally, we give an example to demonstrate the validity of our global asymptotic stability result for the above neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Global robust exponential stability analysis for interval recurrent neural networks

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.; Zou Yun

2004-01-01

This Letter investigates the problem of robust global exponential stability analysis for interval recurrent neural networks (RNNs) via the linear matrix inequality (LMI) approach. The values of the time-invariant uncertain parameters are assumed to be bounded within given compact sets. An improved condition for the existence of a unique equilibrium point and its global exponential stability of RNNs with known parameters is proposed. Based on this, a sufficient condition for the global robust exponential stability for interval RNNs is obtained. Both of the conditions are expressed in terms of LMIs, which can be checked easily by various recently developed convex optimization algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition

Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo

2008-01-01

In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results

Novel results for global robust stability of delayed neural networks

International Nuclear Information System (INIS)

Yucel, Eylem; Arik, Sabri

2009-01-01

This paper investigates the global robust convergence properties of continuous-time neural networks with discrete time delays. By employing suitable Lyapunov functionals, some sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point are derived. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are also given to compare our results with previous robust stability results derived in the literature.

Feedback stabilized tandem Fabry-Perot interferometer

International Nuclear Information System (INIS)

Fukushima, Hiroyuki; Ito, Mikio; Shirasu, Hiroshi.

1986-01-01

A new system for measuring the isotopic ratio of uranium, in which two plane-type Fabry-Perot interferometers (tandem FP) are connected in series. The parallelism between the two FPs is achieved automatically by a feedback control mechanism based on laser interference fringe monitoring. The structure of the tandem FP, feedback control system, automatic parallelism adjustment mechanism and wavelength synchronization mechanism are described in detail. For experiments, a hollow cathode discharge tube of a pulse discharge type is employed. Measurements are made to determine the effects of pulse width on the 238 U peak height of 502.7 nm line, recorder traces of 235 U and 238 U lines, half width for 238 U component of the 502.7 nm line, SN ratio, reproducibility of the 235 U/ 238 U peak height ratio and 235 U/ 238 U intensity ratio. Considerations are made on the spectral line width, contrast, transmission factor, and stability of automatic parallelism control and wavelength synchronization. Results obtained indicates that a single-type interferometer would serve adequately for measuring the 235 U/ 238 U ratio if the automatic parallelism control developed here is used. The ultimate object of the tandem system is to make measurement of 236 U. Satisfactory results have not obtained as yet, but most likely the present system would make it possible if a light source of a higher intensity and advanced photometric techniques are developed. (Nogami, K.)

Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays

International Nuclear Information System (INIS)

Zhao Hongyong; Ding Nan; Chen Ling

2009-01-01

This paper is concerned with the problem of exponential stability analysis for fuzzy cellular neural network with delays. By constructing suitable Lyapunov functional and using stochastic analysis we present some sufficient conditions ensuring almost sure exponential stability for the network. Moreover, an example is given to demonstrate the advantages of our method.

pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

Science.gov (United States)

Wang, Fen; Chen, Yuanlong; Liu, Meichun

2018-02-01

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Periodic Forcing of Inhibition-Stabilized Networks: Nonlinear Resonances and Phase-Amplitude Coupling

OpenAIRE

Veltz, Romain; Sejnowski, Terrence J.

2015-01-01

International audience; Inhibition stabilized networks (ISNs) are neural architectures with strong positive feedback among pyramidal neurons balanced by strong negative feedback from in-hibitory interneurons, a circuit element found in the hippocampus and the primary vi-sual cortex. In their working regime, ISNs produce damped oscillations in the γ-range in response to inputs to the inhibitory population. In order to understand the proper-ties of interconnected ISNs, we investigated periodic ...

Stability and bifurcation of numerical discretization of a second-order delay differential equation with negative feedback

International Nuclear Information System (INIS)

Ding Xiaohua; Su Huan; Liu Mingzhu

2008-01-01

The paper analyzes a discrete second-order, nonlinear delay differential equation with negative feedback. The characteristic equation of linear stability is solved, as a function of two parameters describing the strength of the feedback and the damping in the autonomous system. The existence of local Hopf bifurcations is investigated, and the direction and stability of periodic solutions bifurcating from the Hopf bifurcation of the discrete model are determined by the Hopf bifurcation theory of discrete system. Finally, some numerical simulations are performed to illustrate the analytical results found

Design, implementation and testing of an implantable impedance-based feedback-controlled neural gastric stimulator

International Nuclear Information System (INIS)

Arriagada, A J; Jurkov, A S; Mintchev, M P; Neshev, E; Andrews, C N; Muench, G

2011-01-01

Functional neural gastrointestinal electrical stimulation (NGES) is a methodology of gastric electrical stimulation that can be applied as a possible treatment for disorders such as obesity and gastroparesis. NGES is capable of generating strong lumen-occluding local contractions that can produce retrograde or antegrade movement of gastric content. A feedback-controlled implantable NGES system has been designed, implemented and tested both in laboratory conditions and in an acute animal setting. The feedback system, based on gastric tissue impedance change, is aimed at reducing battery energy requirements and managing the phenomenon of gastric tissue accommodation. Acute animal testing was undertaken in four mongrel dogs (2 M, 2 F, weight 25.53 ± 7.3 kg) that underwent subserosal two-channel electrode implantation. Three force transducers sutured serosally along the gastric axis and a wireless signal acquisition system were utilized to record stimulation-generated contractions and tissue impedance variations respectively. Mechanically induced contractions in the stomach were utilized to indirectly generate a tissue impedance change that was detected by the feedback system. Results showed that increasing or decreasing impedance changes were detected by the implantable stimulator and that therapy can be triggered as a result. The implantable feedback system brings NGES one step closer to long term treatment of burdening gastric motility disorders in humans

Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

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Xuefei Wu

2012-01-01

Full Text Available The generalized projective synchronization (GPS between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control.

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Yang, Shiju; Li, Chuandong; Huang, Tingwen

2016-03-01

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Existence and exponential stability of almost periodic solution for stochastic cellular neural networks with delay

International Nuclear Information System (INIS)

Huang Zaitang; Yang Qigui

2009-01-01

The paper considers the problems of existence of quadratic mean almost periodic and global exponential stability for stochastic cellular neural networks with delays. By employing the Holder's inequality and fixed points principle, we present some new criteria ensuring existence and uniqueness of a quadratic mean almost periodic and global exponential stability. These criteria are important in signal processing and the design of networks. Moreover, these criteria are also applied in others stochastic biological neural systems.

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

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Lam, H K

2012-02-01

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

Stabilization of memory States by stochastic facilitating synapses.

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Miller, Paul

2013-12-06

Bistability within a small neural circuit can arise through an appropriate strength of excitatory recurrent feedback. The stability of a state of neural activity, measured by the mean dwelling time before a noise-induced transition to another state, depends on the neural firing-rate curves, the net strength of excitatory feedback, the statistics of spike times, and increases exponentially with the number of equivalent neurons in the circuit. Here, we show that such stability is greatly enhanced by synaptic facilitation and reduced by synaptic depression. We take into account the alteration in times of synaptic vesicle release, by calculating distributions of inter-release intervals of a synapse, which differ from the distribution of its incoming interspike intervals when the synapse is dynamic. In particular, release intervals produced by a Poisson spike train have a coefficient of variation greater than one when synapses are probabilistic and facilitating, whereas the coefficient of variation is less than one when synapses are depressing. However, in spite of the increased variability in postsynaptic input produced by facilitating synapses, their dominant effect is reduced synaptic efficacy at low input rates compared to high rates, which increases the curvature of neural input-output functions, leading to wider regions of bistability in parameter space and enhanced lifetimes of memory states. Our results are based on analytic methods with approximate formulae and bolstered by simulations of both Poisson processes and of circuits of noisy spiking model neurons.

Feedback-stabilized fractional fringe laser interferometer for plasma density measurements

International Nuclear Information System (INIS)

Schneider, J.; Robertson, S.

1979-01-01

A feedback stabilization technique is described for a fractional fringe interferometer measuring plasma electron densities. Using this technique, a CO 2 laser Michelson interferometer with a pyroelectric detector exhibited a sensitivity of 3.4 x 10 -4 fringe on a 1-ms time scale and, due to acoustic pickup, 1.8 x 10 -2 fringe on a 10-ms time scale. The rise time is 45 μs. Stabilization against slow drifts in mirror distances is achieved by an electromechanically translated mirror driven by a servo system having a 0.2-s response time. A mechanical chopper in one of the two beam paths generates the signal which drives the servo system

Improved asymptotic stability analysis for uncertain delayed state neural networks

International Nuclear Information System (INIS)

Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

2009-01-01

This paper presents a new linear matrix inequality (LMI) based approach to the stability analysis of artificial neural networks (ANN) subject to time-delay and polytope-bounded uncertainties in the parameters. The main objective is to propose a less conservative condition to the stability analysis using the Gu's discretized Lyapunov-Krasovskii functional theory and an alternative strategy to introduce slack matrices. Two computer simulations examples are performed to support the theoretical predictions. Particularly, in the first example, the Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability. The second example is presented to illustrate how the proposed approach can provide better stability performance when compared to other ones in the literature

Exponential stability of Cohen-Grossberg neural networks with a general class of activation functions

International Nuclear Information System (INIS)

Wan Anhua; Wang Miansen; Peng Jigen; Qiao Hong

2006-01-01

In this Letter, the dynamics of Cohen-Grossberg neural networks model are investigated. The activation functions are only assumed to be Lipschitz continuous, which provide a much wider application domain for neural networks than the previous results. By means of the extended nonlinear measure approach, new and relaxed sufficient conditions for the existence, uniqueness and global exponential stability of equilibrium of the neural networks are obtained. Moreover, an estimate for the exponential convergence rate of the neural networks is precisely characterized. Our results improve those existing ones

An Integrative, Multi-Scale Computational Model of a Swimming Lamprey Fully Coupled to Its Fluid Environment and Incorporating Proprioceptive Feedback

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Hamlet, C. L.; Hoffman, K.; Fauci, L.; Tytell, E.

2016-02-01

The lamprey is a model organism for both neurophysiology and locomotion studies. To study the role of sensory feedback as an organism moves through its environment, a 2D, integrative, multi-scale model of an anguilliform swimmer driven by neural activation from a central pattern generator (CPG) is constructed. The CPG in turn drives muscle kinematics and is fully coupled to the surrounding fluid. The system is numerically evolved in time using an immersed boundary framework producing an emergent swimming mode. Proprioceptive feedback to the CPG based on experimental observations adjust the activation signal as the organism interacts with its environment. Effects on the speed, stability and cost (metabolic work) of swimming due to nonlinear dependencies associated with muscle force development combined with proprioceptive feedback to neural activation are estimated and examined.

Boundedness and stability for recurrent neural networks with variable coefficients and time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

In this Letter, the problems of boundedness and stability for a general class of non-autonomous recurrent neural networks with variable coefficients and time-varying delays are analyzed via employing Young inequality technique and Lyapunov method. Some simple sufficient conditions are given for boundedness and stability of the solutions for the recurrent neural networks. These results generalize and improve the previous works, and they are easy to check and apply in practice. Two illustrative examples and their numerical simulations are also given to demonstrate the effectiveness of the proposed results

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.

Robust stability for stochastic bidirectional associative memory neural networks with time delays

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Shu, H. S.; Lv, Z. W.; Wei, G. L.

2008-02-01

In this paper, the asymptotic stability is considered for a class of uncertain stochastic bidirectional associative memory neural networks with time delays and parameter uncertainties. The delays are time-invariant and the uncertainties are norm-bounded that enter into all network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov-Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed criteria.

Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

Science.gov (United States)

Toyomaki, Atsuhito; Hashimoto, Naoki; Kako, Yuki; Murohashi, Harumitsu; Kusumi, Ichiro

2017-01-01

Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

Stability and oscillation of two coupled Duffing equations with time delay state feedback

International Nuclear Information System (INIS)

El-Bassiouny, A F

2006-01-01

This paper presents an analytical study of the simultaneous principal parametric resonances of two coupled Duffing equations with time delay state feedback. The concept of an equivalent damping related to the delay feedback is proposed and the appropriate choice of the feedback gains and the time delay is discussed from the viewpoint of vibration control. The method of multiple scales is used to determine a set of ordinary differential equations governing the modulation of the amplitudes and phases of the two modes. The first order approximation of the resonances are derived and the effect of time delay on the resonances is investigated. The fixed points correspond to a periodic motion for the starting system and we show the frequency-response curves. We analyse the effect of time delay and the other different parameters on these oscillations. The stability of the fixed points is examined by using the variational method. Numerical solutions are carried out and graphical representations of the results are presented and discussed. Increasing in the time delay τ given decreasing and increasing in the regions of definition and stability respectively and the first mode has decreased magnitudes. The multivalued solutions disappear when decreasing the coefficients of cubic nonlinearities of the second mode α 3 and the detuning parameter σ 2 respectively. Both modes shift to the left for increasing linear feedback gain v 1 and the coefficient of parametric excitation f 1 respectively

Global exponential stability of BAM neural networks with time-varying delays: The discrete-time case

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Raja, R.; Marshal Anthoni, S.

2011-02-01

This paper deals with the problem of stability analysis for a class of discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient conditions is proposed for the global exponential stability of discrete-time BAM neural networks. The proposed LMI based results can be easily checked by LMI control toolbox. Moreover, an example is also provided to demonstrate the effectiveness of the proposed method.

Core stabilization exercise with real-time feedback for chronic hemiparetic stroke: a pilot randomized controlled trials.

Science.gov (United States)

Chung, Eunjung; Lee, Byoung-Hee; Hwang, Sujin

2014-01-01

The purpose of this study was to examine the feasibility of core stabilization exercise with real-time feedback on balance and gait function in patients with chronic hemiparetic stroke. Nineteen stroke subjects were enrolled in this study. The patients were randomly divided into the experimental (n = 10) and control groups (n = 9). Subjects in the experimental group performed core stabilization exercise with real-time feedback training for 30 minutes per day during a period of six weeks. Subjects in the control group performed core stabilization exercise during the same period. This study assessed the kinematic parameters using a portable walkway system, and timed up-and-go test. Gait velocity showed significantly greater improvement in the experimental group (7.3 ± 5.0 sec) than in the control group (-0.7 ± 10.6). Stride length showed significantly greater increase in the experimental group (13.2 ± 7.9 on the affected side and 12.6 ± 8.0 on the less affected side) than the control group (3.5 ± 8.7 on the affected side and 3.4 ± 8.5 on the less affected side). After training, change in results on the timed up and go test was significantly greater in the experimental group than in the control group. Core stabilization exercise using real-time feedback produces greater improvement in gait performance in chronic hemiparetic stroke patients than core stabilization exercise only.

Improved result on stability analysis of discrete stochastic neural networks with time delay

International Nuclear Information System (INIS)

Wu Zhengguang; Su Hongye; Chu Jian; Zhou Wuneng

2009-01-01

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.

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

Directory of Open Access Journals (Sweden)

Feng Zhao

2015-01-01

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

Diagnostic of Gravitropism-like Stabilizer of Inspection Drone Using Neural Networks

Science.gov (United States)

Kruglova, Tatyana; Sayfeddine, Daher; Bulgakov, Alexey

2018-03-01

This paper discusses the enhancement of flight stability of using an inspection drone to scan the condition of buildings on low and high altitude. Due to aerial perturbations and wakes, the drone starts to shake and may be damaged. One of the mechanical optimization methods it so add a built-in stabilizing mechanism. However, the performance of this supporting device becomes critical on certain flying heights, thus to avoid losing the drone. The paper is divided in two parts: the description of the gravitropism-like stabilizer and the diagnostic of its status using wavelet transformation and neural network classification.

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

Science.gov (United States)

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Artificial Neural Network Compensated Output Feedback Power-Level Control for Modular High Temperature Gas-Cooled Reactors

Directory of Open Access Journals (Sweden)

Zhe Dong

2014-02-01

Full Text Available Small modular reactors (SMRs could be beneficial in providing electricity power safely and also be viable for applications such as seawater desalination and heat production. Due to its inherent safety features, the modular high temperature gas-cooled reactor (MHTGR has been seen as one of the best candidates for building SMR-based nuclear power plants. Since the MHTGR dynamics display high nonlinearity and parameter uncertainty, it is necessary to develop a nonlinear adaptive power-level control law which is not only beneficial to the safe, stable, efficient and autonomous operation of the MHTGR, but also easy to implement practically. In this paper, based on the concept of shifted-ectropy and the physically-based control design approach, it is proved theoretically that the simple proportional-differential (PD output-feedback power-level control can provide asymptotic closed-loop stability. Then, based on the strong approximation capability of the multi-layer perceptron (MLP artificial neural network (ANN, a compensator is established to suppress the negative influence caused by system parameter uncertainty. It is also proved that the MLP-compensated PD power-level control law constituted by an experientially-tuned PD regulator and this MLP-based compensator can guarantee bounded closed-loop stability. Numerical simulation results not only verify the theoretical results, but also illustrate the high performance of this MLP-compensated PD power-level controller in suppressing the oscillation of process variables caused by system parameter uncertainty.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

Science.gov (United States)

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Global exponential stability and periodicity of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo; Lu Linji

2009-01-01

In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.

Finite-time stability of neutral-type neural networks with random time-varying delays

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Ali, M. Syed; Saravanan, S.; Zhu, Quanxin

2017-11-01

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov-Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.

Cultural shaping of neural responses: Feedback-related potentials vary with self-construal and face priming.

Science.gov (United States)

Hitokoto, Hidefumi; Glazer, James; Kitayama, Shinobu

2016-01-01

Previous work shows that when an image of a face is presented immediately prior to each trial of a speeded cognitive task (face-priming), the error-related negativity (ERN) is upregulated for Asians, but it is downregulated for Caucasians. These findings are consistent with the hypothesis that images of "generalized other" vary cross-culturally such that they evoke anxiety for Asians, whereas they serve as safety cues for Caucasians. Here, we tested whether the cross-cultural variation in the face-priming effect would be observed in a gambling paradigm. Caucasian Americans, Asian Americans, and Asian sojourners were exposed to a brief flash of a schematic face during a gamble. For Asian Americans, face-priming resulted in significant increases of both negative-going deflection of ERP upon negative feedback (feedback-related negativity [FRN]) and positive-going deflection of ERP upon positive feedback (feedback-related positivity [FRP]). For Caucasian Americans, face-priming showed a significant reversal, decreasing both FRN and FRP. The cultural difference in the face-priming effect in FRN and FRP was partially mediated by interdependent self-construal. Curiously, Asian sojourners showed a pattern similar to the one for Caucasian Americans. Our findings suggest that culture shapes neural pathways in both systematic and highly dynamic fashion. © 2015 Society for Psychophysiological Research.

Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

Directory of Open Access Journals (Sweden)

Atsuhito Toyomaki

Full Text Available Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System

Science.gov (United States)

Shadmehr, Reza

2016-01-01

When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system. SIGNIFICANCE STATEMENT Our sensory organs transduce errors in behavior. To improve performance, we must generate better motor commands. How does the nervous system transform an error in sensory coordinates into better motor commands in muscle coordinates? Here we show that when an error occurs during a movement, the reflexes transform the sensory representation of error into motor

Finite-time stabilization of uncertain nonholonomic systems in feedforward-like form by output feedback.

Science.gov (United States)

Gao, Fangzheng; Wu, Yuqiang; Zhang, Zhongcai

2015-11-01

This paper investigates the problem of finite-time stabilization by output feedback for a class of nonholonomic systems in chained form with uncertainties. Comparing with the existing relevant literature, a distinguishing feature of the systems under investigation is that the x-subsystem is a feedforward-like rather than feedback-like system. This renders the existing control methods inapplicable to the control problems of the systems. A constructive design procedure for output feedback control is given. The designed controller renders that the states of closed-loop system are regulated to zero in a finite time. Two simulation examples are provided to illustrate the effectiveness of the proposed approach. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A simple toroidal shell model for the study of feedback stabilization of resistive wall modes in a tokamak plasma

International Nuclear Information System (INIS)

Jhang, Hogun

2008-01-01

A study is conducted on the feedback stabilization of resistive wall modes (RWMs) in a tokamak plasma using a toroidal shell model. An analytically tractable form of the RWM dispersion relation is derived in the presence of a set of discrete feedback coil currents. A parametric study is carried out to optimize the feedback system configuration. It is shown that the total toroidal angle of a resistive wall spanned by the feedback coils and the poloidal angular extent of a feedback coil are crucial parameters to determine the efficacy of the feedback system

Stability and selectivity of a chronic, multi-contact cuff electrode for sensory stimulation in human amputees.

Science.gov (United States)

Tan, Daniel W; Schiefer, Matthew A; Keith, Michael W; Anderson, J Robert; Tyler, Dustin J

2015-04-01

Stability and selectivity are important when restoring long-term, functional sensory feedback in individuals with limb-loss. Our objective is to demonstrate a chronic, clinical neural stimulation system for providing selective sensory response in two upper-limb amputees. Multi-contact cuff electrodes were implanted in the median, ulnar, and radial nerves of the upper-limb. Nerve stimulation produced a selective sensory response on 19 of 20 contacts and 16 of 16 contacts in subjects 1 and 2, respectively. Stimulation elicited multiple, distinct percept areas on the phantom and residual limb. Consistent threshold, impedance, and percept areas have demonstrated that the neural interface is stable for the duration of this on-going, chronic study. We have achieved selective nerve response from multi-contact cuff electrodes by demonstrating characteristic percept areas and thresholds for each contact. Selective sensory response remains consistent in two upper-limb amputees for 1 and 2 years, the longest multi-contact sensory feedback system to date. Our approach demonstrates selectivity and stability can be achieved through an extraneural interface, which can provide sensory feedback to amputees.

On global stability criterion for neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Park, Ju H.

2006-01-01

Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new delay-dependent criterion for neural networks with discrete and distributed delays is derived to guarantee global asymptotic stability. The criterion is expressed in terms of LMIs, which can be solved easily by various convex optimization algorithms. Some numerical examples are given to show the effectiveness of proposed method

Plasma measurement by feedback-stabilized dual-beam laser interferometer

International Nuclear Information System (INIS)

Yasuda, Akio; Kawahata, Kazuo; Kanai, Yasubumi.

1982-03-01

The plasma density in a dynamic magneto arcjet is measured by a stabilized dual-beam laser interferometer proposed by the authors. The fringe shift for a 0.63 μm beam of He-Ne laser is used to stabilize the interferometer against the effect of mechanical vibration by means of a feedback controlled speaker coil, while the other beam of 3.39 μm, for which the effect of mechanical vibrations is excluded, is used to measure plasma density. Stability of --1/500 of one fringe for 0.63 μm is obtained during a long period for frequencies lower than a few Hertz. Stability for higher frequencies, which determines the accuracy of the present measurement, is limited to --1/30 of one fringe for 0.63 μm, which corresponds to --1/200 of one fringe and a line electron density of --1.5 x 10 14 cm - 2 for 3.39 μm, by acoustic noise picked up by the speaker coil. The advantage of this technique over the single-laser technique is that the frequency response of the interferometer extends down to zero frequency. Since the effect of the neutral gas background is practically reduced to zero, the present interferometer is to be applied advantageously to the diagnostics of the plasma produced in high pressure gases. (author)

Gaze stabilization in chronic vestibular-loss and in cerebellar ataxia: interactions of feedforward and sensory feedback mechanisms.

Science.gov (United States)

Sağlam, M; Lehnen, N

2014-01-01

During gaze shifts, humans can use visual, vestibular, and proprioceptive feedback, as well as feedforward mechanisms, for stabilization against active and passive head movements. The contributions of feedforward and sensory feedback control, and the role of the cerebellum, are still under debate. To quantify these contributions, we increased the head moment of inertia in three groups (ten healthy, five chronic vestibular-loss and nine cerebellar-ataxia patients) while they performed large gaze shifts to flashed targets in darkness. This induces undesired head oscillations. Consequently, both active (desired) and passive (undesired) head movements had to be compensated for to stabilize gaze. All groups compensated for active and passive head movements, vestibular-loss patients less than the other groups (P feedforward mechanisms substantially contribute to gaze stabilization. Proprioception alone is not sufficient (gain 0.2). Stabilization against active and passive head movements was not impaired in our cerebellar ataxia patients.

Comprehensive joint feedback control for standing by functional neuromuscular stimulation-a simulation study.

Science.gov (United States)

Nataraj, Raviraj; Audu, Musa L; Kirsch, Robert F; Triolo, Ronald J

2010-12-01

Previous investigations of feedback control of standing after spinal cord injury (SCI) using functional neuromuscular stimulation (FNS) have primarily targeted individual joints. This study assesses the potential efficacy of comprehensive (trunk, hips, knees, and ankles) joint feedback control against postural disturbances using a bipedal, 3-D computer model of SCI stance. Proportional-derivative feedback drove an artificial neural network trained to produce muscle excitation patterns consistent with maximal joint stiffness values achievable about neutral stance given typical SCI muscle properties. Feedback gains were optimized to minimize upper extremity (UE) loading required to stabilize against disturbances. Compared to the baseline case of maximum constant muscle excitations used clinically, the controller reduced UE loading by 55% in resisting external force perturbations and by 84% during simulated one-arm functional tasks. Performance was most sensitive to inaccurate measurements of ankle plantar/dorsiflexion position and hip ab/adduction velocity feedback. In conclusion, comprehensive joint feedback demonstrates potential to markedly improve FNS standing function. However, alternative control structures capable of effective performance with fewer sensor-based feedback parameters may better facilitate clinical usage.

Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties.

Science.gov (United States)

Song, Qiankun; Yu, Qinqin; Zhao, Zhenjiang; Liu, Yurong; Alsaadi, Fuad E

2018-07-01

In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, sufficient condition to guarantee the boundedness of networks and the existence, uniqueness and global robust stability of equilibrium point is derived for the considered uncertain neural networks. The obtained robust stability criterion is expressed in complex-valued LMI, which can be calculated numerically using YALMIP with solver of SDPT3 in MATLAB. An example with simulations is supplied to show the applicability and advantages of the acquired result. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

International Nuclear Information System (INIS)

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

2009-01-01

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity. (author)

The Derivation of the Stability Bound of the Feedback ANC System That Has an Error in the Estimated Secondary Path Model

Directory of Open Access Journals (Sweden)

Seong-Pil Moon

2018-01-01

Full Text Available This paper investigates the stability problem of the feedback active noise control (ANC system, which can be caused by the modeling error of the electro-acoustic path estimation in its feedback mechanism. A stability analysis method is proposed to obtain the stability bound as a form of a closed-form equation in terms of the delay error length of the secondary path, the ANC filter length, and the primary noise frequency. In the proposed method, the system’s open loop magnitude and phase response equations are separately exploited and approximated within the Nyquist stability criterion. The stability bound of the proposed method is verified by comparing both the original Nyquist stability condition and the simulation results.

Delay-Dependent Stability Criteria of Uncertain Periodic Switched Recurrent Neural Networks with Time-Varying Delays

Directory of Open Access Journals (Sweden)

Xing Yin

2011-01-01

uncertain periodic switched recurrent neural networks with time-varying delays. When uncertain discrete-time recurrent neural network is a periodic system, it is expressed as switched neural network for the finite switching state. Based on the switched quadratic Lyapunov functional approach (SQLF and free-weighting matrix approach (FWM, some linear matrix inequality criteria are found to guarantee the delay-dependent asymptotical stability of these systems. Two examples illustrate the exactness of the proposed criteria.

Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay

International Nuclear Information System (INIS)

Feng Wei; Yang, Simon X.; Fu Wei; Wu Haixia

2009-01-01

This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria.

Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

International Nuclear Information System (INIS)

Wan Li; Zhou Qinghua

2007-01-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established

Global exponential stability of BAM neural networks with time-varying delays and diffusion terms

Science.gov (United States)

Wan, Li; Zhou, Qinghua

2007-11-01

The stability property of bidirectional associate memory (BAM) neural networks with time-varying delays and diffusion terms are considered. By using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium solution of such networks are established.

Control of uncertain systems by feedback linearization with neural networks augmentation. Part II. Controller validation by numerical simulation

Directory of Open Access Journals (Sweden)

Adrian TOADER

2010-09-01

Full Text Available The paper was conceived in two parts. Part I, previously published in this journal, highlighted the main steps of adaptive output feedback control for non-affine uncertain systems, having a known relative degree. The main paradigm of this approach was the feedback linearization (dynamic inversion with neural network augmentation. Meanwhile, based on new contributions of the authors, a new paradigm, that of robust servomechanism problem solution, has been added to the controller architecture. The current Part II of the paper presents the validation of the controller hereby obtained by using the longitudinal channel of a hovering VTOL-type aircraft as mathematical model.

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

International Nuclear Information System (INIS)

Arik, Sabri

2006-01-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature

Global asymptotic stability of hybrid bidirectional associative memory neural networks with time delays

Science.gov (United States)

Arik, Sabri

2006-02-01

This Letter presents a sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with distributed time delays. The results impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. The results are also compared with the previous results derived in the literature.

Local and global stability for Lotka-Volterra systems with distributed delays and instantaneous negative feedbacks

Science.gov (United States)

Faria, Teresa; Oliveira, José J.

This paper addresses the local and global stability of n-dimensional Lotka-Volterra systems with distributed delays and instantaneous negative feedbacks. Necessary and sufficient conditions for local stability independent of the choice of the delay functions are given, by imposing a weak nondelayed diagonal dominance which cancels the delayed competition effect. The global asymptotic stability of positive equilibria is established under conditions slightly stronger than the ones required for the linear stability. For the case of monotone interactions, however, sharper conditions are presented. This paper generalizes known results for discrete delays to systems with distributed delays. Several applications illustrate the results.

Stabilizing operation point technique based on the tunable distributed feedback laser for interferometric sensors

Science.gov (United States)

Mao, Xuefeng; Zhou, Xinlei; Yu, Qingxu

2016-02-01

We describe a stabilizing operation point technique based on the tunable Distributed Feedback (DFB) laser for quadrature demodulation of interferometric sensors. By introducing automatic lock quadrature point and wavelength periodically tuning compensation into an interferometric system, the operation point of interferometric system is stabilized when the system suffers various environmental perturbations. To demonstrate the feasibility of this stabilizing operation point technique, experiments have been performed using a tunable-DFB-laser as light source to interrogate an extrinsic Fabry-Perot interferometric vibration sensor and a diaphragm-based acoustic sensor. Experimental results show that good tracing of Q-point was effectively realized.

Comparing the neural basis of monetary reward and cognitive feedback during information-integration category learning.

Science.gov (United States)

Daniel, Reka; Pollmann, Stefan

2010-01-06

The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.

Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Linshan; Zhang Zhe; Wang Yangfan

2008-01-01

Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities

Feedback Networks

OpenAIRE

Zamir, Amir R.; Wu, Te-Lin; Sun, Lin; Shen, William; Malik, Jitendra; Savarese, Silvio

2016-01-01

Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration's...

Existence and exponential stability of almost periodic solution for Hopfield-type neural networks with impulse

International Nuclear Information System (INIS)

Zhang Huiying; Xia Yonghui

2008-01-01

In this paper, some sufficient conditions are obtained for checking the existence and exponential stability of almost periodic solution for bidirectional associative memory Hopfield-type neural networks with impulse. The approaches are based on contraction principle and Gronwall-Bellman's inequality. This paper is considering the almost periodic solution for impulsive Hopfield-type neural networks

An estimation of the domain of attraction and convergence rate for Hopfield continuous feedback neural networks

International Nuclear Information System (INIS)

Cao Jinde

2004-01-01

In this Letter, the domain of attraction of memory patterns and exponential convergence rate of the network trajectories to memory patterns for Hopfield continuous associative memory are estimated by means of matrix measure and comparison principle. A new estimation is given for the domain of attraction of memory patterns and exponential convergence rate. These results can be used for the evaluation of fault-tolerance capability and the synthesis procedures for Hopfield continuous feedback associative memory neural networks

Global robust asymptotical stability of multi-delayed interval neural networks: an LMI approach

International Nuclear Information System (INIS)

Li Chuandong; Liao Xiaofeng; Zhang Rong

2004-01-01

Based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria for interval neural networks (IDNN) with multiple time-varying delays are derived to guarantee global robust asymptotic stability. The main results are generalizations of some recent results reported in the literature. Numerical example is also given to show the effectiveness of our results

On exponential stability of bidirectional associative memory neural networks with time-varying delays

International Nuclear Information System (INIS)

Park, Ju H.; Lee, S.M.; Kwon, O.M.

2009-01-01

For bidirectional associate memory neural networks with time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the Lyapunov functional method and linear matrix inequality (LMI) technique. A novel criterion for the stability, which give information on the delay-dependent property, is derived. A numerical example is given to demonstrate the effectiveness of the obtained results.

Electroencephalogy (EEG) Feedback in Decision-Making

Science.gov (United States)

2015-08-26

Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful

Comprehensive Joint Feedback Control for Standing by Functional Neuromuscular Stimulation – a Simulation Study

Science.gov (United States)

Nataraj, Raviraj; Audu, Musa L.; Kirsch, Robert F.; Triolo, Ronald J.

2013-01-01

Previous investigations of feedback control of standing after spinal cord injury (SCI) using functional neuromuscular stimulation (FNS) have primarily targeted individual joints. This study assesses the potential efficacy of comprehensive (trunk, hips, knees, and ankles) joint-feedback control against postural disturbances using a bipedal, three-dimensional computer model of SCI stance. Proportional-derivative feedback drove an artificial neural network trained to produce muscle excitation patterns consistent with maximal joint stiffness values achievable about neutral stance given typical SCI muscle properties. Feedback gains were optimized to minimize upper extremity (UE) loading required to stabilize against disturbances. Compared to the baseline case of maximum constant muscle excitations used clinically, the controller reduced UE loading by 55% in resisting external force perturbations and by 84% during simulated one-arm functional tasks. Performance was most sensitive to inaccurate measurements of ankle plantar/dorsiflexion position and hip ab/adduction velocity feedback. In conclusion, comprehensive joint-feedback demonstrates potential to markedly improve FNS standing function. However, alternative control structures capable of effective performance with fewer sensor-based feedback parameters may better facilitate clinical usage. PMID:20923741

Novel global robust stability criteria for interval neural networks with multiple time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.

2005-01-01

This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method

Stability investigations of the ASDEX feedback system with filters for reducing thyristor noise

International Nuclear Information System (INIS)

Crisanti, F.; Schneider, F.

1983-06-01

A computer program for analysing the absolute and relative stabilities of any complex system by the root-locus method was developed. It is used to reanalyse the present horizontal position feed-back control in the ASDEX tokamak and to select the optimum parameters for this system with RCL filters for reducing thyristor noise. (orig.)

Robust stability analysis of switched Hopfield neural networks with time-varying delay under uncertainty

International Nuclear Information System (INIS)

Huang He; Qu Yuzhong; Li Hanxiong

2005-01-01

With the development of intelligent control, switched systems have been widely studied. Here we try to introduce some ideas of the switched systems into the field of neural networks. In this Letter, a class of switched Hopfield neural networks with time-varying delay is investigated. The parametric uncertainty is considered and assumed to be norm bounded. Firstly, the mathematical model of the switched Hopfield neural networks is established in which a set of Hopfield neural networks are used as the individual subsystems and an arbitrary switching rule is assumed; Secondly, robust stability analysis for such switched Hopfield neural networks is addressed based on the Lyapunov-Krasovskii approach. Some criteria are given to guarantee the switched Hopfield neural networks to be globally exponentially stable for all admissible parametric uncertainties. These conditions are expressed in terms of some strict linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate our results

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Xiaohu; Xu Daoyi

2009-01-01

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.

New results for global robust stability of bidirectional associative memory neural networks with multiple time delays

International Nuclear Information System (INIS)

Senan, Sibel; Arik, Sabri

2009-01-01

This paper presents some new sufficient conditions for the global robust asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with multiple time delays. The results we obtain impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. We also give some numerical examples to demonstrate the applicability and effectiveness of our results, and compare the results with the previous robust stability results derived in the literature.

An improved direct feedback linearization technique for transient stability enhancement and voltage regulation of power generators

Energy Technology Data Exchange (ETDEWEB)

Kenne, Godpromesse [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, Universite de Dschang, B.P. 134 Bandjoun, Cameroun; Goma, Raphael; Lamnabhi-Lagarrigue, Francoise [Laboratoire des Signaux et Systemes (L2S), CNRS-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Nkwawo, Homere [Departement GEII, Universite Paris XIII, IUT Villetaneuse, 99 Avenue Jean Baptiste Clement, 93430 Villetaneuse (France); Arzande, Amir; Vannier, Jean Claude [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)

2010-09-15

In this paper, a simple improved direct feedback linearization design method for transient stability and voltage regulation of power systems is discussed. Starting with the classical direct feedback linearization technique currently applied to power systems, an adaptive nonlinear excitation control of synchronous generators is proposed, which is new and effective for engineering. The power angle and mechanical power input are not assumed to be available. The proposed method is based on a standard third-order model of a synchronous generator which requires only information about the physical available measurements of angular speed, active electric power and generator terminal voltage. Experimental results of a practical power system show that fast response, robustness, damping, steady-state and transient stability as well as voltage regulation are all achieved satisfactorily. (author)

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

2008-01-01

In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks.

Science.gov (United States)

Wu, Ailong; Zeng, Zhigang

2016-02-01

We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Neural correlates of feedback processing in toddlers

NARCIS (Netherlands)

Meyer, M.; Bekkering, H.; Janssen, D.J.C.; Bruijn, E.R.A. de; Hunnius, S.

2014-01-01

External feedback provides essential information for successful learning. Feedback is especially important for learning in early childhood, as toddlers strongly rely on external signals to determine the consequences of their actions. In adults, many electrophysiological studies have elucidated

Novel stability criteria for uncertain delayed Cohen-Grossberg neural networks using discretized Lyapunov functional

International Nuclear Information System (INIS)

Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

2009-01-01

This paper deals with the stability analysis of delayed uncertain Cohen-Grossberg neural networks (CGNN). The proposed methodology consists in obtaining new robust stability criteria formulated as linear matrix inequalities (LMIs) via the Lyapunov-Krasovskii theory. Particularly one stability criterion is derived from the selection of a parameter-dependent Lyapunov-Krasovskii functional, which allied with the Gu's discretization technique and a simple strategy that decouples the system matrices from the functional matrices, assures a less conservative stability condition. Two computer simulations are presented to support the improved theoretical results.

Robust stability of interval bidirectional associative memory neural network with time delays.

Science.gov (United States)

Liao, Xiaofeng; Wong, Kwok-wo

2004-04-01

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

Science.gov (United States)

Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

2015-01-01

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

Dynamics of nonlinear feedback control

NARCIS (Netherlands)

Snippe, H.P.; Hateren, J.H. van

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain

Will they like me? Neural and behavioral responses to social-evaluative peer feedback in socially and non-socially anxious females.

Science.gov (United States)

van der Molen, Melle J W; Harrewijn, Anita; Westenberg, P Michiel

2018-03-07

The current study examined neural and behavioral responses to social-evaluative feedback processing in social anxiety. Twenty-two non-socially and 17 socially anxious females (mean age = 19.57 years) participated in a Social Judgment Paradigm in which they received peer acceptance/rejection feedback that was either congruent or incongruent with their prior predictions. Results indicated that socially anxious participants believed they would receive less social acceptance feedback than non-socially anxious participants. EEG results demonstrated that unexpected social rejection feedback elicited a significant increase in theta (4-8 Hz) power relative to other feedback conditions. This theta response was only observed in non-socially anxious individuals. Together, results corroborate cognitive-behavioral studies demonstrating a negative expectancy bias in socially anxiety with respect to social evaluation. Furthermore, the present findings highlight a functional role for theta oscillatory dynamics in processing cues that convey social-evaluative threat, and this social threat-monitoring mechanism seems less sensitive in socially anxious females. Copyright © 2018 Elsevier B.V. All rights reserved.

Feedback stabilization system for pulsed single longitudinal mode tunable lasers

Science.gov (United States)

Esherick, Peter; Raymond, Thomas D.

1991-10-01

A feedback stabilization system for pulse single longitudinal mode tunable lasers having an excited laser medium contained within an adjustable length cavity and producing a laser beam through the use of an internal dispersive element, including detection of angular deviation in the output laser beam resulting from detuning between the cavity mode frequency and the passband of the internal dispersive element, and generating an error signal based thereon. The error signal can be integrated and amplified and then applied as a correcting signal to a piezoelectric transducer mounted on a mirror of the laser cavity for controlling the cavity length.

A feedback model of visual attention.

Science.gov (United States)

Spratling, M W; Johnson, M H

2004-03-01

Feedback connections are a prominent feature of cortical anatomy and are likely to have a significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain, our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

2009-01-01

In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

Feedback-stabilized dual-beam laser interferometer for plasma measurements

International Nuclear Information System (INIS)

Yasuda, A.; Kanai, Y.; Kusunoki, J.; Kawahata, K.; Takeda, S.

1980-01-01

A stabilized laser interferometer is proposed with two beams as the light source. The fringe shift for a 0.63 μm beam of a He--Ne laser is used to stabilize the interferometer against the effect of mechanical vibrations via a feedback controlled speaker coil, while another beam of 3.39 μm, for which consequently the effect of the mechanical vibrations is excluded, is used to measure the plasma density. A stability of approx.1/500 of one fringe for 0.63 μm is obtained during a long period for frequencies lower than a few Hz. The stability for higher frequencies is limited to approx.1/30 of one fringe for 0.63 μm, which correspondes to approx.1/200 of one fringe for 3.39 μm, by the acoustic noise picked up by the speaker coil. Furthermore, the total accuracy is limited by the detector noise to approx.1/60 of one fringe for 3.39 μm, which corresponds to a line electron density of approx.5 x 10 14 cm -2 . The detector noise may be reduced by cooling the detector. The advantage of this technique over the single-laser technique is that the frequency response of the interferometer extends down to zero frequency. The interferometer is tested with the measurement of a plasma in a dynamic magnetic arcjet. Since the effect of the neutral gas background is reduced in the present interferometer, the application has an advantage for the diagnostics of plasmas produced in high pressure gases

Using DCM pitch modulation and feedback to improve long term X-ray beam stability

International Nuclear Information System (INIS)

Bloomer, C; Dent, A; Diaz-Moreno, S; Dolbnya, I; Pedersen, U; Rehm, G; Tang, C; Thomas, C

2013-01-01

In this paper we demonstrate significant improvements to the stability of the monochromatic X-ray beam intensity on several beamlines at Diamond, using a modulation of the pitch axis of the DCM with a piezoelectric actuator. The modulation is detected on an intensity diagnostic (e.g. an ion chamber) using a software lock-in technique. The detected amplitude and phase are used in a feedback to keep the DCM at the peak of the rocking curve, or any arbitrary position 'off-peak' which might be desired to detune the DCM and reject unwanted harmonics. A major advantage of this software based system is the great flexibility offered, using standard, readily available instrumentation. Measurements of the short and long-term performance of the feedback on several beamlines are presented, and the limitations of such a feedback are discussed.

STABILIZING CLOUD FEEDBACK DRAMATICALLY EXPANDS THE HABITABLE ZONE OF TIDALLY LOCKED PLANETS

International Nuclear Information System (INIS)

Yang Jun; Abbot, Dorian S.; Cowan, Nicolas B.

2013-01-01

The habitable zone (HZ) is the circumstellar region where a planet can sustain surface liquid water. Searching for terrestrial planets in the HZ of nearby stars is the stated goal of ongoing and planned extrasolar planet surveys. Previous estimates of the inner edge of the HZ were based on one-dimensional radiative-convective models. The most serious limitation of these models is the inability to predict cloud behavior. Here we use global climate models with sophisticated cloud schemes to show that due to a stabilizing cloud feedback, tidally locked planets can be habitable at twice the stellar flux found by previous studies. This dramatically expands the HZ and roughly doubles the frequency of habitable planets orbiting red dwarf stars. At high stellar flux, strong convection produces thick water clouds near the substellar location that greatly increase the planetary albedo and reduce surface temperatures. Higher insolation produces stronger substellar convection and therefore higher albedo, making this phenomenon a stabilizing climate feedback. Substellar clouds also effectively block outgoing radiation from the surface, reducing or even completely reversing the thermal emission contrast between dayside and nightside. The presence of substellar water clouds and the resulting clement surface conditions will therefore be detectable with the James Webb Space Telescope.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

International Nuclear Information System (INIS)

Xu Chang-Jin; Li Pei-Luan; Pang Yi-Cheng

2017-01-01

This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. (paper)

Design, parametrization, and pole placement of stabilizing output feedback compensators via injective cogenerator quotient signal modules.

Science.gov (United States)

Blumthaler, Ingrid; Oberst, Ulrich

2012-03-01

Control design belongs to the most important and difficult tasks of control engineering and has therefore been treated by many prominent researchers and in many textbooks, the systems being generally described by their transfer matrices or by Rosenbrock equations and more recently also as behaviors. Our approach to controller design uses, in addition to the ideas of our predecessors on coprime factorizations of transfer matrices and on the parametrization of stabilizing compensators, a new mathematical technique which enables simpler design and also new theorems in spite of the many outstanding results of the literature: (1) We use an injective cogenerator signal module ℱ over the polynomial algebra [Formula: see text] (F an infinite field), a saturated multiplicatively closed set T of stable polynomials and its quotient ring [Formula: see text] of stable rational functions. This enables the simultaneous treatment of continuous and discrete systems and of all notions of stability, called T-stability. We investigate stabilizing control design by output feedback of input/output (IO) behaviors and study the full feedback IO behavior, especially its autonomous part and not only its transfer matrix. (2) The new technique is characterized by the permanent application of the injective cogenerator quotient signal module [Formula: see text] and of quotient behaviors [Formula: see text] of [Formula: see text]-behaviors B. (3) For the control tasks of tracking, disturbance rejection, model matching, and decoupling and not necessarily proper plants we derive necessary and sufficient conditions for the existence of proper stabilizing compensators with proper and stable closed loop behaviors, parametrize all such compensators as IO behaviors and not only their transfer matrices and give new algorithms for their construction. Moreover we solve the problem of pole placement or spectral assignability for the complete feedback behavior. The properness of the full feedback behavior

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

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Yuzheng Yang

2014-01-01

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

Global Mittag-Leffler stability analysis of fractional-order impulsive neural networks with one-side Lipschitz condition.

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Zhang, Xinxin; Niu, Peifeng; Ma, Yunpeng; Wei, Yanqiao; Li, Guoqiang

2017-10-01

This paper is concerned with the stability analysis issue of fractional-order impulsive neural networks. Under the one-side Lipschitz condition or the linear growth condition of activation function, the existence of solution is analyzed respectively. In addition, the existence, uniqueness and global Mittag-Leffler stability of equilibrium point of the fractional-order impulsive neural networks with one-side Lipschitz condition are investigated by the means of contraction mapping principle and Lyapunov direct method. Finally, an example with numerical simulation is given to illustrate the validity and feasibility of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays

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Fei Chen

2013-01-01

Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.

Optimal control of nonlinear continuous-time systems in strict-feedback form.

Science.gov (United States)

Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani

2015-10-01

This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

Impulsive effects on global asymptotic stability of delay BAM neural networks

International Nuclear Information System (INIS)

Chen Jun; Cui Baotong

2008-01-01

Based on the proper Lyapunov functions and the Jacobsthal liner inequality, some sufficient conditions are presented in this paper for global asymptotic stability of delay bidirectional associative memory neural networks with impulses. The obtained results are independently of the delay parameters and can be easily verified. Also, some remarks and an illustrative example are given to demonstrate the effectiveness of the obtained results

Stability analysis of delayed Cohen-Grossberg BAM neural networks with impulses via nonsmooth analysis

International Nuclear Information System (INIS)

Wen Zhen; Sun Jitao

2009-01-01

In this paper, we investigate the existence and uniqueness of equilibrium point for delayed Cohen-Grossberg bidirectional associative memory (BAM) neural networks with impulses, based on nonsmooth analysis method. And we give the criteria of global exponential stability of the unique equilibrium point for the delayed BAM neural networks with impulses using Lyapunov method. The new sufficient condition generalizes and improves the previously known results. Finally, we present examples to illustrate that our results are effective.

Exponential p-stability of delayed Cohen-Grossberg-type BAM neural networks with impulses

International Nuclear Information System (INIS)

Xia Yonghui; Huang Zhenkun; Han Maoan

2008-01-01

An impulsive Cohen-Grossberg-type bidirectional associative memory (BAM) neural networks with distributed delays is studied. Some new sufficient conditions are established for the existence and global exponential stability of a unique equilibrium without strict conditions imposed on self regulation functions. The approaches are based on Laypunov-Kravsovskii functional and homeomorphism theory. When our results are applied to the BAM neural networks, our results generalize some previously known results. It is believed that these results are significant and useful for the design and applications of Cohen-Grossberg-type bidirectional associative memory networks

Global exponential stability analysis on impulsive BAM neural networks with distributed delays

Science.gov (United States)

Li, Yao-Tang; Yang, Chang-Bo

2006-12-01

Using M-matrix and topological degree tool, sufficient conditions are obtained for the existence, uniqueness and global exponential stability of the equilibrium point of bidirectional associative memory (BAM) neural networks with distributed delays and subjected to impulsive state displacements at fixed instants of time by constructing a suitable Lyapunov functional. The results remove the usual assumptions that the boundedness, monotonicity, and differentiability of the activation functions. It is shown that in some cases, the stability criteria can be easily checked. Finally, an illustrative example is given to show the effectiveness of the presented criteria.

Neurophysiological correlates of anhedonia in feedback processing

Science.gov (United States)

Mies, Gabry W.; Van den Berg, Ivo; Franken, Ingmar H. A.; Smits, Marion; Van der Molen, Maurits W.; Van der Veen, Frederik M.

2013-01-01

Disturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a key role have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study investigated the relations between the two core symptoms of depression, i.e., depressed mood and anhedonia, and the neural correlates of feedback processing using fMRI. The focus was on the different subdivisions of the anterior cingulate cortex (ACC). Undergraduates with varying levels of depressed mood and anhedonia performed a time-estimation task in which they received positive and negative feedback that was either valid or invalid (i.e., related vs. unrelated to actual performance). The rostral cingulate zone (RCZ), corresponding to the dorsal part of the ACC, was less active in response to feedback in more anhedonic individuals, after correcting for the influence of depressed mood, whereas the subgenual ACC was more active in these individuals. Task performance was not affected by anhedonia, however. No statistically significant effects were found for depressed mood above and beyond the effects of anhedonia. This study therefore implies that increasing levels of anhedonia involve changes in the neural circuitry underlying feedback processing. PMID:23532800

Global exponential stability of cellular neural networks with mixed delays and impulses

International Nuclear Information System (INIS)

Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua

2007-01-01

In this paper cellular neural networks with mixed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results

An analysis of global robust stability of uncertain cellular neural networks with discrete and distributed delays

International Nuclear Information System (INIS)

Park, Ju H.

2007-01-01

This paper considers the robust stability analysis of cellular neural networks with discrete and distributed delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, a novel stability criterion guaranteeing the global robust convergence of the equilibrium point is derived. The criterion can be solved easily by various convex optimization algorithms. An example is given to illustrate the usefulness of our results

Feedback systems in the SLC

International Nuclear Information System (INIS)

Thompson, K.A.; Jobe, R.K.; Johnson, R.; Phinney, N.

1987-02-01

Two classes of computer-controlled feedback have been implemented to stabilize parameters in subsystems of the SLC: (1) ''slow'' (time scales ∼ minutes) feedback, and (2) ''fast'', i.e., pulse-to-pulse, feedback. The slow loops run in a single FEEDBACK process in the SLC host VAX, which acquires signals and sets control parameters via communication with the database and the network of normal SLC microprocessors. Slow loops exist to stabilize beam energy and energy spread, beam position and angle, and timing of kicker magnets, and to compensate for changes in the phase length of the rf drive line. The fast loops run in dedicated microprocessors, and may sample and/or feedback on particular parameters as often as every pulse of the SLC beam. The first implementations of fast feedback are to control transverse beam blow-up and to stabilize the energy and energy spread of bunches going into the SLC arcs. The overall architecture of the feedback software and the operator interface for controlling loops are discussed

Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

Science.gov (United States)

Huang, Haiying; Du, Qiaosheng; Kang, Xibing

2013-11-01

In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Effects of mechanical feedback on the stability of cardiac scroll waves: A bidomain electro-mechanical simulation study.

Science.gov (United States)

Colli Franzone, P; Pavarino, L F; Scacchi, S

2017-09-01

In this work, we investigate the influence of cardiac tissue deformation on re-entrant wave dynamics. We have developed a 3D strongly coupled electro-mechanical Bidomain model posed on an ideal monoventricular geometry, including fiber direction anisotropy and stretch-activated currents (SACs). The cardiac mechanical deformation influences the bioelectrical activity with two main mechanical feedback: (a) the geometric feedback (GEF) due to the presence of the deformation gradient in the diffusion coefficients and in a convective term depending on the deformation rate and (b) the mechano-electric feedback (MEF) due to SACs. Here, we investigate the relative contribution of these two factors with respect to scroll wave stability. We extend the previous works [Keldermann et al., Am. J. Physiol. Heart Circ. Physiol. 299, H134-H143 (2010) and Hu et al., PLoS One 8(4), e60287 (2013)] that were based on the Monodomain model and a simple non-selective linear SAC, while here we consider the full Bidomain model and both selective and non-selective components of SACs. Our simulation results show that the stability of cardiac scroll waves is influenced by MEF, which in case of low reversal potential of non-selective SACs might be responsible for the onset of ventricular fibrillation; GEF increases the scroll wave meandering but does not determine the scroll wave stability.

Delay-Dependent Stability Criterion for Bidirectional Associative Memory Neural Networks with Interval Time-Varying Delays

Science.gov (United States)

Park, Ju H.; Kwon, O. M.

In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.

Global asymptotic stability of Cohen-Grossberg neural network with continuously distributed delays

International Nuclear Information System (INIS)

Wan Li; Sun Jianhua

2005-01-01

The convergence dynamical behaviors of Cohen-Grossberg neural network with continuously distributed delays are discussed. By using Brouwer's fixed point theorem, matrix theory and analysis techniques such as Gronwall inequality, some new sufficient conditions guaranteeing the existence, uniqueness of an equilibrium point and its global asymptotic stability are obtained. An example is given to illustrate the theoretical results

Exponential stability of fuzzy cellular neural networks with constant and time-varying delays

International Nuclear Information System (INIS)

Liu Yanqing; Tang Wansheng

2004-01-01

In this Letter, the global stability of delayed fuzzy cellular neural networks (FCNN) with either constant delays or time varying delays is proposed. Firstly, we give the existence and uniqueness of the equilibrium point by using the theory of topological degree and the properties of nonsingular M-matrix and the sufficient conditions for ascertaining the global exponential stability by constructing a suitable Lyapunov functional. Secondly, the criteria for guaranteeing the global exponential stability of FCNN with time varying delays are given and the estimation of exponential convergence rate with regard to speed of vary of delays is presented by constructing a suitable Lyapunov functional

Global robust stability of neural networks with multiple discrete delays and distributed delays

International Nuclear Information System (INIS)

Gao Ming; Cui Baotong

2009-01-01

The problem of global robust stability is investigated for a class of uncertain neural networks with both multiple discrete time-varying delays and distributed time-varying delays. The uncertainties are assumed to be of norm-bounded form and the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov stability theory and linear matrix inequality technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. Two examples are given to show the effectiveness of the proposed results.

Dynamics of nonlinear feedback control

OpenAIRE

Snippe, H.P.; Hateren, J.H. van

2007-01-01

Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input step...

Existence and stability of periodic solution in impulsive Hopfield neural networks with finite distributed delays

International Nuclear Information System (INIS)

Yang Xiaofan; Liao Xiaofeng; Evans, David J.; Tang Yuanyan

2005-01-01

In this Letter, we introduce a class of Hopfield neural networks with periodic impulses and finite distributed delays. We then derive a sufficient condition for the existence and global exponential stability of a unique periodic solution of the networks, which assumes neither the differentiability nor the monotonicity of the activation functions. Our condition extends and generalizes a known condition for the global exponential periodicity of continuous Hopfield neural networks with finite distributed delays

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

Science.gov (United States)

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

2017-03-01

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

Robust stability of uncertain Markovian jumping Cohen-Grossberg neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Sheng Li; Yang Huizhong

2009-01-01

This paper considers the robust stability of a class of uncertain Markovian jumping Cohen-Grossberg neural networks (UMJCGNNs) with mixed time-varying delays. The parameter uncertainties are norm-bounded and the mixed time-varying delays comprise discrete and distributed time delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. An example is given to show the effectiveness of the proposed results.

Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

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Hongli Liu

2009-01-01

Full Text Available We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.

Real-time system for studies of the effects of acoustic feedback on animal vocalizations.

Directory of Open Access Journals (Sweden)

Mike eSkocik

2013-01-01

Full Text Available Studies of behavioral and neural responses to distorted auditory feedback can help shed light on the neural mechanisms of animal vocalizations. We describe an apparatus for generating real-time acoustic feedback. The system can very rapidly detect acoustic features in a song and output acoustic signals if the detected features match the desired acoustic template. The system uses spectrogram-based detection of acoustic elements. It is low-cost and can be programmed for a variety of behavioral experiments requiring acoustic feedback or neural stimulation. We use the system to study the effects of acoustic feedback on birds' vocalizations and demonstrate that such an acoustic feedback can cause both immediate and long-term changes to birds’ songs.

Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays

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Yonggang Chen

2008-01-01

Full Text Available This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI. Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.

Dynamic feedback for multi-mode plasma instabilities

International Nuclear Information System (INIS)

Sen, A.K.

1978-01-01

Constant feedback, which has been used exclusively, fails to stabilize more than one mode of a plasma instability. It is shown that a suitable dynamic or frequency-dependent feedback can stabilize all modes. Methods are developed in which such a feedback structure can be chosen in terms of its poles and zeros in relation to those of the plasma transfer function in the complex frequency plane. The synthesis procedure for such a feedback structure, in the form of an integrated electronic circuit is also discussed. As an example, a dynamic feedback for multi-mode stabilization of a collisional drift wave instability is developed in detail. (author)

Novel criteria for global exponential periodicity and stability of recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Song Qiankun

2008-01-01

In this paper, the global exponential periodicity and stability of recurrent neural networks with time-varying delays are investigated by applying the idea of vector Lyapunov function, M-matrix theory and inequality technique. We assume neither the global Lipschitz conditions on these activation functions nor the differentiability on these time-varying delays, which were needed in other papers. Several novel criteria are found to ascertain the existence, uniqueness and global exponential stability of periodic solution for recurrent neural network with time-varying delays. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. Some previous results are improved and generalized, and an example is given to show the effectiveness of our method

Robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays

International Nuclear Information System (INIS)

Balasubramaniam, P.; Lakshmanan, S.; Manivannan, A.

2012-01-01

Highlights: ► Robust stability analysis for Markovian jumping interval neural networks is considered. ► Both linear fractional and interval uncertainties are considered. ► A new LKF is constructed with triple integral terms. ► MATLAB LMI control toolbox is used to validate theoretical results. ► Numerical examples are given to illustrate the effectiveness of the proposed method. - Abstract: This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.

A new criterion for global robust stability of interval neural networks with discrete time delays

International Nuclear Information System (INIS)

Li Chuandong; Chen Jinyu; Huang Tingwen

2007-01-01

This paper further studies global robust stability of a class of interval neural networks with discrete time delays. By introducing an equivalent transformation of interval matrices, a new criterion on global robust stability is established. In comparison with the results reported in the literature, the proposed approach leads to results with less restrictive conditions. Numerical examples are also worked through to illustrate our results

Estimation of exponential convergence rate and exponential stability for neural networks with time-varying delay

International Nuclear Information System (INIS)

Tu Fenghua; Liao Xiaofeng

2005-01-01

We study the problem of estimating the exponential convergence rate and exponential stability for neural networks with time-varying delay. Some criteria for exponential stability are derived by using the linear matrix inequality (LMI) approach. They are less conservative than the existing ones. Some analytical methods are employed to investigate the bounds on the interconnection matrix and activation functions so that the systems are exponentially stable

Exponential stability for stochastic delayed recurrent neural networks with mixed time-varying delays and impulses: the continuous-time case

International Nuclear Information System (INIS)

Karthik Raja, U; Leelamani, A; Raja, R; Samidurai, R

2013-01-01

In this paper, the exponential stability for a class of stochastic neural networks with time-varying delays and impulsive effects is considered. By constructing suitable Lyapunov functionals and by using the linear matrix inequality optimization approach, we obtain sufficient delay-dependent criteria to ensure the exponential stability of stochastic neural networks with time-varying delays and impulses. Two numerical examples with simulation results are provided to illustrate the effectiveness of the obtained results over those already existing in the literature. (paper)

Constrained quadratic stabilization of discrete-time uncertain nonlinear multi-model systems using piecewise affine state-feedback

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Olav Slupphaug

1999-07-01

Full Text Available In this paper a method for nonlinear robust stabilization based on solving a bilinear matrix inequality (BMI feasibility problem is developed. Robustness against model uncertainty is handled. In different non-overlapping regions of the state-space called clusters the plant is assumed to be an element in a polytope which vertices (local models are affine systems. In the clusters containing the origin in their closure, the local models are restricted to be linear systems. The clusters cover the region of interest in the state-space. An affine state-feedback is associated with each cluster. By utilizing the affinity of the local models and the state-feedback, a set of linear matrix inequalities (LMIs combined with a single nonconvex BMI are obtained which, if feasible, guarantee quadratic stability of the origin of the closed-loop. The feasibility problem is attacked by a branch-and-bound based global approach. If the feasibility check is successful, the Liapunov matrix and the piecewise affine state-feedback are given directly by the feasible solution. Control constraints are shown to be representable by LMIs or BMIs, and an application of the control design method to robustify constrained nonlinear model predictive control is presented. Also, the control design method is applied to a simple example.

New results on global exponential stability of recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Chu Yuming; Lu Junwei

2006-01-01

This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples

New results on global exponential stability of recurrent neural networks with time-varying delays

Energy Technology Data Exchange (ETDEWEB)

Xu Shengyuan [Department of Automation, Nanjing University of Science and Technology, Nanjing 210094 (China)]. E-mail: syxu02@yahoo.com.cn; Chu Yuming [Department of Mathematics, Huzhou Teacher' s College, Huzhou, Zhejiang 313000 (China); Lu Junwei [School of Electrical and Automation Engineering, Nanjing Normal University, 78 Bancang Street, Nanjing, 210042 (China)

2006-04-03

This Letter provides new sufficient conditions for the existence, uniqueness and global exponential stability of the equilibrium point of recurrent neural networks with time-varying delays by employing Lyapunov functions and using the Halanay inequality. The time-varying delays are not necessarily differentiable. Both Lipschitz continuous activation functions and monotone nondecreasing activation functions are considered. The derived stability criteria are expressed in terms of linear matrix inequalities (LMIs), which can be checked easily by resorting to recently developed algorithms solving LMIs. Furthermore, the proposed stability results are less conservative than some previous ones in the literature, which is demonstrated via some numerical examples.

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

OpenAIRE

Wei Feng; Simon X. Yang; Haixia Wu

2014-01-01

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported ...

Analysis of Stiffened Penstock External Pressure Stability Based on Immune Algorithm and Neural Network

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Wensheng Dong

2014-01-01

Full Text Available The critical external pressure stability calculation of stiffened penstock in the hydroelectric power station is very important work for penstock design. At present, different assumptions and boundary simplification are adopted by different calculation methods which sometimes cause huge differences too. In this paper, we present an immune based artificial neural network model via the model and stability theory of elastic ring, we study effects of some factors (such as pipe diameter, pipe wall thickness, sectional size of stiffening ring, and spacing between stiffening rings on penstock critical external pressure during huge thin-wall procedure of penstock. The results reveal that the variation of diameter and wall thickness can lead to sharp variation of penstock external pressure bearing capacity and then give the change interval of it. This paper presents an optimizing design method to optimize sectional size and spacing of stiffening rings and to determine penstock bearing capacity coordinate with the bearing capacity of stiffening rings and penstock external pressure stability coordinate with its strength safety. As a practical example, the simulation results illustrate that the method presented in this paper is available and can efficiently overcome inherent defects of BP neural network.

Global exponential stability of cellular neural networks with continuously distributed delays and impulses

International Nuclear Information System (INIS)

Wang Yixuan; Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua

2006-01-01

In this Letter cellular neural networks with continuously distributed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality techniques. The results of this Letter are new and they complement previously known results

Numerical Study of Equilibrium, Stability, and Advanced Resistive Wall Mode Feedback Algorithms on KSTAR

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Katsuro-Hopkins, Oksana; Sabbagh, S. A.; Bialek, J. M.; Park, H. K.; Kim, J. Y.; You, K.-I.; Glasser, A. H.; Lao, L. L.

2007-11-01

Stability to ideal MHD kink/ballooning modes and the resistive wall mode (RWM) is investigated for the KSTAR tokamak. Free-boundary equilibria that comply with magnetic field coil current constraints are computed for monotonic and reversed shear safety factor profiles and H-mode tokamak pressure profiles. Advanced tokamak operation at moderate to low plasma internal inductance shows that a factor of two improvement in the plasma beta limit over the no-wall beta limit is possible for toroidal mode number of unity. The KSTAR conducting structure, passive stabilizers, and in-vessel control coils are modeled by the VALEN-3D code and the active RWM stabilization performance of the device is evaluated using both standard and advanced feedback algorithms. Steady-state power and voltage requirements for the system are estimated based on the expected noise on the RWM sensor signals. Using NSTX experimental RWM sensors noise data as input, a reduced VALEN state-space LQG controller is designed to realistically assess KSTAR stabilization system performance.

Numerical simulation of feedback stabilization of axisymmetric modes in tokamaks using driven halo currents

International Nuclear Information System (INIS)

Jardin, S.C.; Schmidt, J.A.

1998-01-01

The Tokamak Simulation Code (TSC) has been used to model a new method of feedback stabilization of the axisymmetric instability in tokamaks using driven halo (or scrape-off layer) currents. The method appears to be feasible for a wide range of plasma edge parameters. It may offer advantages over the more conventional method of controlling this instability when applied in a reactor environment. (author)

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

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Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2008-01-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

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Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach

International Nuclear Information System (INIS)

Li Chuandong; Liao Xiaofeng; Zhang Rong

2005-01-01

For bi-directional associative memory (BAM) neural networks (NNs) with different constant or time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated in this paper. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problems, which provide bounds on the interconnection matrix and the activation functions, so as to guarantee the system's exponential stability. Some criteria for the exponential stability, which give information on the delay-dependent property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the exponential stability of delayed BAM (DBAM) neural networks, which are less conservative and less restrictive than the ones reported so far in the literature. Some typical examples are presented to show the application of the criteria obtained in this paper

Stability and bifurcation analysis for a discrete-time bidirectional ring neural network model with delay

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Yan-Ke Du

2013-09-01

Full Text Available We study a class of discrete-time bidirectional ring neural network model with delay. We discuss the asymptotic stability of the origin and the existence of Neimark-Sacker bifurcations, by analyzing the corresponding characteristic equation. Employing M-matrix theory and the Lyapunov functional method, global asymptotic stability of the origin is derived. Applying the normal form theory and the center manifold theorem, the direction of the Neimark-Sacker bifurcation and the stability of bifurcating periodic solutions are obtained. Numerical simulations are given to illustrate the main results.

Negative feedback mechanism for the long-term stabilization of earth's surface temperature

International Nuclear Information System (INIS)

Walker, J.C.G.; Hays, P.B.; Kasting, J.F.

1981-01-01

We suggest that the partial pressure of carbon dioxide in the atmosphere is buffered, over geological time scales, by a negative feedback mechanism in which the rate of weathering of silicate minerals (followed by deposition of carbonate minerals) depends on surface temperature, and surface temperature, in turn, depends on carbon dioxide partial pressure through the green effect. Although the quantitative details of this mechanism are speculative, it appears able partially to stabilize earth's surface temperature against the steady increase of solar luminosity believed to have occured since the origin of the solar system

A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach

International Nuclear Information System (INIS)

Cao Jinde; Ho, Daniel W.C.

2005-01-01

In this paper, global asymptotic stability is discussed for neural networks with time-varying delay. Several new criteria in matrix inequality form are given to ascertain the uniqueness and global asymptotic stability of equilibrium point for neural networks with time-varying delay based on Lyapunov method and Linear Matrix Inequality (LMI) technique. The proposed LMI approach has the advantage of considering the difference of neuronal excitatory and inhibitory efforts, which is also computationally efficient as it can be solved numerically using recently developed interior-point algorithm. In addition, the proposed results generalize and improve previous works. The obtained criteria also combine two existing conditions into one generalized condition in matrix form. An illustrative example is also given to demonstrate the effectiveness of the proposed results

Neural activation during imitation with or without performance feedback: An fMRI study.

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Zhang, Kaihua; Wang, Hui; Dong, Guangheng; Wang, Mengxing; Zhang, Jilei; Zhang, Hui; Meng, Weixia; Du, Xiaoxia

2016-08-26

In our daily lives, we often receive performance feedback (PF) during imitative learning, and we adjust our behaviors accordingly to improve performance. However, little is known regarding the neural mechanisms underlying this learning process. We hypothesized that appropriate PF would enhance neural activation or recruit additional brain areas during subsequent action imitation. Pictures of 20 different finger gestures without any social meaning were shown to participants from the first-person perspective. Imitation with or without PF was investigated by functional magnetic resonance imaging in 30 healthy subjects. The PF was given by a real person or by a computer. PF from a real person induced hyperactivation of the parietal lobe (precuneus and cuneus), cingulate cortex (posterior and anterior), temporal lobe (superior and transverse temporal gyri), and cerebellum (posterior and anterior lobes) during subsequent imitation. The positive PF and negative PF from a real person, induced the activation of more brain areas during the following imitation. The hyperactivation of the cerebellum, posterior cingulate cortex, precuneus, and cuneus suggests that the subjects exhibited enhanced motor control and visual attention during imitation after PF. Additionally, random PF from a computer had a small effect on the next imitation. We suggest that positive and accurate PF may be helpful for imitation learning. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Delay-dependent exponential stability for neural networks with discrete and distributed time-varying delays

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Zhu Xunlin; Wang Youyi

2009-01-01

This Letter studies the exponential stability for a class of neural networks (NNs) with both discrete and distributed time-varying delays. Under weaker assumptions on the activation functions, by defining a more general type of Lyapunov functionals and developing a new convex combination technique, new less conservative and less complex stability criteria are established to guarantee the global exponential stability of the discussed NNs. The obtained conditions are dependent on both discrete and distributed delays, are expressed in terms of linear matrix inequalities (LMIs), and contain fewer decision variables. Numerical examples are given to illustrate the effectiveness and the less conservatism of the proposed conditions.

Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

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Yongkun Li

2009-01-01

Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.

Global asymptotic stability analysis of bidirectional associative memory neural networks with distributed delays and impulse

International Nuclear Information System (INIS)

Huang Zaitang; Luo Xiaoshu; Yang Qigui

2007-01-01

Many systems existing in physics, chemistry, biology, engineering and information science can be characterized by impulsive dynamics caused by abrupt jumps at certain instants during the process. These complex dynamical behaviors can be model by impulsive differential system or impulsive neural networks. This paper formulates and studies a new model of impulsive bidirectional associative memory (BAM) networks with finite distributed delays. Several fundamental issues, such as global asymptotic stability and existence and uniqueness of such BAM neural networks with impulse and distributed delays, are established

Delayed feedback control of fractional-order chaotic systems

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Gjurchinovski, A; Urumov, V; Sandev, T

2010-01-01

We study the possibility to stabilize unstable steady states and unstable periodic orbits in chaotic fractional-order dynamical systems by the time-delayed feedback method. By performing a linear stability analysis, we establish the parameter ranges for successful stabilization of unstable equilibria in the plane parameterized by the feedback gain and the time delay. An insight into the control mechanism is gained by analyzing the characteristic equation of the controlled system, showing that the control scheme fails to control unstable equilibria having an odd number of positive real eigenvalues. We demonstrate that the method can also stabilize unstable periodic orbits for a suitable choice of the feedback gain, providing that the time delay is chosen to coincide with the period of the target orbit. In addition, it is shown numerically that delayed feedback control with a sinusoidally modulated time delay significantly enlarges the stability region of steady states in comparison to the classical time-delayed feedback scheme with a constant delay.

Mittag-Leffler synchronization of delayed fractional-order bidirectional associative memory neural networks with discontinuous activations: state feedback control and impulsive control schemes.

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Ding, Xiaoshuai; Cao, Jinde; Zhao, Xuan; Alsaadi, Fuad E

2017-08-01

This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.

Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

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Ferris Daniel P

2010-12-01

Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

Stability and bifurcation of a discrete BAM neural network model with delays

International Nuclear Information System (INIS)

Zheng Baodong; Zhang Yang; Zhang Chunrui

2008-01-01

A map modelling a discrete bidirectional associative memory neural network with delays is investigated. Its dynamics is studied in terms of local analysis and Hopf bifurcation analysis. By analyzing the associated characteristic equation, its linear stability is investigated and Hopf bifurcations are demonstrated. It is found that there exist Hopf bifurcations when the delay passes a sequence of critical values. Numerical simulation is performed to verify the analytical results

Global exponential stability of fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Qianhong; Luo Wei

2009-01-01

In this paper, a class of fuzzy bidirectional associated memory (BAM) neural networks with time-varying delays are studied. Employing fixed point theorem, matrix theory and inequality analysis, some sufficient conditions are established for the existence, uniqueness and global exponential stability of equilibrium point. The sufficient conditions are easy to verify at pattern recognition and automatic control. Finally, an example is given to show feasibility and effectiveness of our results.

Stability of Almost Periodic Solution for a General Class of Discontinuous Neural Networks with Mixed Time-Varying Delays

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Yingwei Li

2013-01-01

Full Text Available The global exponential stability issues are considered for almost periodic solution of the neural networks with mixed time-varying delays and discontinuous neuron activations. Some sufficient conditions for the existence, uniqueness, and global exponential stability of almost periodic solution are achieved in terms of certain linear matrix inequalities (LMIs, by applying differential inclusions theory, matrix inequality analysis technique, and generalized Lyapunov functional approach. In addition, the existence and asymptotically almost periodic behavior of the solution of the neural networks are also investigated under the framework of the solution in the sense of Filippov. Two simulation examples are given to illustrate the validity of the theoretical results.

Robust stability analysis of Takagi—Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

International Nuclear Information System (INIS)

Ali, M. Syed

2011-01-01

In this paper, the global stability of Takagi—Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature. (general)

Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Li Zuoan; Li Kelin

2009-01-01

In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

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Qiankun Song

2007-06-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

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Cao Jinde

2007-01-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Effects of linear and nonlinear time-delayed feedback on the noise-enhanced stability phenomenon in a periodically driven bistable system

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Jia, Zheng-Lin; Mei, Dong-Cheng

2011-01-01

We investigate numerically the effects of time delay on the phenomenon of noise-enhanced stability (NES) in a periodically modulated bistable system. Three types of time-delayed feedback, including linear delayed feedback, nonlinear delayed feedback and global delayed feedback, are considered. We find a non-monotonic behaviour of the mean first-passage time (MFPT) as a function of the delay time τ, with a maximum in the case of linear delayed feedback and with a minimum in the case of nonlinear delayed feedback. There are two peculiar values of τ around which the NES phenomenon is enhanced or weakened. For the case of global delayed feedback, the increase of τ always weakens the NES phenomenon. Moreover, we also show that the amplitude A and the frequency Ω of the periodic forcing play an opposite role in the NES phenomenon, i.e. the increase of A weakens the NES effect while the increase of Ω enhances it. These observations demonstrate that the time-delayed feedback can be used as a feasible control scheme for the NES phenomenon

Sliding Intermittent Control for BAM Neural Networks with Delays

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Jianqiang Hu

2013-01-01

Full Text Available This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

Stability and Hopf bifurcation in a simplified BAM neural network with two time delays.

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Cao, Jinde; Xiao, Min

2007-03-01

Various local periodic solutions may represent different classes of storage patterns or memory patterns, and arise from the different equilibrium points of neural networks (NNs) by applying Hopf bifurcation technique. In this paper, a bidirectional associative memory NN with four neurons and multiple delays is considered. By applying the normal form theory and the center manifold theorem, analysis of its linear stability and Hopf bifurcation is performed. An algorithm is worked out for determining the direction and stability of the bifurcated periodic solutions. Numerical simulation results supporting the theoretical analysis are also given.

DCS-Neural-Network Program for Aircraft Control and Testing

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Jorgensen, Charles C.

2006-01-01

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

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Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

2009-12-01

The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

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

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

2009-10-01

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

Feedback control of coupled-bunch instabilities

International Nuclear Information System (INIS)

Fox, J.D.; Eisen, N.; Hindi, H.; Linscott, I.; Oxoby, G.; Sapozhnikov, L.; Serio, M.

1993-05-01

The next generation of synchrotron light sources and particle accelerators will require active feedback systems to control multi-bunch instabilities. Stabilizing hundreds or thousands of potentially unstable modes in these accelerator designs presents many technical challenges. Feedback systems to stabilize coupled-bunch instabilities may be understood in the frequency domain (mode-based feedback) or in the time domain (bunch-by-bunch feedback). In both approaches an external amplifier system is used to create damping fields that prevent coupled-bunch oscillations from growing without bound. The system requirements for transverse (betatron) and longitudinal (synchrotron) feedback are presented, and possible implementation options developed. Feedback system designs based on digital signal-processing techniques are described. Experimental results are shown from a synchrotron oscillation damper in the SSRL/SLAC storage ring SPEAR that uses digital signal-processing techniques

Dissociation between active and observational learning from positive and negative feedback in Parkinsonism.

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Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina; Südmeyer, Martin; Bellebaum, Christian

2012-01-01

Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson's Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson's Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson's Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.

Ideomotor feedback control in a recurrent neural network.

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Galtier, Mathieu

2015-06-01

The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction.

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Sivakumar, Seshadri; Sivakumar, Shyamala

2017-09-25

This paper introduces a discrete-time recurrent neural network architecture using triangular feedback weight matrices that allows a simplified approach to ensuring network and training stability. The triangular structure of the weight matrices is exploited to readily ensure that the eigenvalues of the feedback weight matrix represented by the block diagonal elements lie on the unit circle in the complex z-plane by updating these weights based on the differential of the angular error variable. Such placement of the eigenvalues together with the extended close interaction between state variables facilitated by the nondiagonal triangular elements, enhances the learning ability of the proposed architecture. Simulation results show that the proposed architecture is highly effective in time-series prediction tasks associated with nonlinear and chaotic dynamic systems with underlying oscillatory modes. This modular architecture with dual upper and lower triangular feedback weight matrices mimics fully recurrent network architectures, while maintaining learning stability with a simplified training process. While training, the block-diagonal weights (hence the eigenvalues) of the dual triangular matrices are constrained to the same values during weight updates aimed at minimizing the possibility of overfitting. The dual triangular architecture also exploits the benefit of parsing the input and selectively applying the parsed inputs to the two subnetworks to facilitate enhanced learning performance.

Numerical Feedback Stabilization with Applications to Networks

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Simone Göttlich

2017-01-01

Full Text Available The focus is on the numerical consideration of feedback boundary control problems for linear systems of conservation laws including source terms. We explain under which conditions the numerical discretization can be used to design feedback boundary values for network applications such as electric transmission lines or traffic flow systems. Several numerical examples illustrate the properties of the results for different types of networks.

Feedback associated with expectation for larger-reward improves visuospatial working memory performances in children with ADHD.

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Hammer, Rubi; Tennekoon, Michael; Cooke, Gillian E; Gayda, Jessica; Stein, Mark A; Booth, James R

2015-08-01

We tested the interactive effect of feedback and reward on visuospatial working memory in children with ADHD. Seventeen boys with ADHD and 17 Normal Control (NC) boys underwent functional magnetic resonance imaging (fMRI) while performing four visuospatial 2-back tasks that required monitoring the spatial location of letters presented on a display. Tasks varied in reward size (large; small) and feedback availability (no-feedback; feedback). While the performance of NC boys was high in all conditions, boys with ADHD exhibited higher performance (similar to those of NC boys) only when they received feedback associated with large-reward. Performance pattern in both groups was mirrored by neural activity in an executive function neural network comprised of few distinct frontal brain regions. Specifically, neural activity in the left and right middle frontal gyri of boys with ADHD became normal-like only when feedback was available, mainly when feedback was associated with large-reward. When feedback was associated with small-reward, or when large-reward was expected but feedback was not available, boys with ADHD exhibited altered neural activity in the medial orbitofrontal cortex and anterior insula. This suggests that contextual support normalizes activity in executive brain regions in children with ADHD, which results in improved working memory. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Lagrange α-exponential stability and α-exponential convergence for fractional-order complex-valued neural networks.

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Jian, Jigui; Wan, Peng

2017-07-01

This paper deals with the problem on Lagrange α-exponential stability and α-exponential convergence for a class of fractional-order complex-valued neural networks. To this end, some new fractional-order differential inequalities are established, which improve and generalize previously known criteria. By using the new inequalities and coupling with the Lyapunov method, some effective criteria are derived to guarantee Lagrange α-exponential stability and α-exponential convergence of the addressed network. Moreover, the framework of the α-exponential convergence ball is also given, where the convergence rate is related to the parameters and the order of differential of the system. These results here, which the existence and uniqueness of the equilibrium points need not to be considered, generalize and improve the earlier publications and can be applied to monostable and multistable fractional-order complex-valued neural networks. Finally, one example with numerical simulations is given to show the effectiveness of the obtained results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks.

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Hu, Cheng; Yu, Juan; Chen, Zhanheng; Jiang, Haijun; Huang, Tingwen

2017-05-01

In this paper, the fixed-time stability of dynamical systems and the fixed-time synchronization of coupled discontinuous neural networks are investigated under the framework of Filippov solution. Firstly, by means of reduction to absurdity, a theorem of fixed-time stability is established and a high-precision estimation of the settling-time is given. It is shown by theoretic proof that the estimation bound of the settling time given in this paper is less conservative and more accurate compared with the classical results. Besides, as an important application, the fixed-time synchronization of coupled neural networks with discontinuous activation functions is proposed. By designing a discontinuous control law and using the theory of differential inclusions, some new criteria are derived to ensure the fixed-time synchronization of the addressed coupled networks. Finally, two numerical examples are provided to show the effectiveness and validity of the theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

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Wei Feng

2014-01-01

Full Text Available The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Fine-tuning and the stability of recurrent neural networks.

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David MacNeil

Full Text Available A central criticism of standard theoretical approaches to constructing stable, recurrent model networks is that the synaptic connection weights need to be finely-tuned. This criticism is severe because proposed rules for learning these weights have been shown to have various limitations to their biological plausibility. Hence it is unlikely that such rules are used to continuously fine-tune the network in vivo. We describe a learning rule that is able to tune synaptic weights in a biologically plausible manner. We demonstrate and test this rule in the context of the oculomotor integrator, showing that only known neural signals are needed to tune the weights. We demonstrate that the rule appropriately accounts for a wide variety of experimental results, and is robust under several kinds of perturbation. Furthermore, we show that the rule is able to achieve stability as good as or better than that provided by the linearly optimal weights often used in recurrent models of the integrator. Finally, we discuss how this rule can be generalized to tune a wide variety of recurrent attractor networks, such as those found in head direction and path integration systems, suggesting that it may be used to tune a wide variety of stable neural systems.

The breaking of a delayed ring neural network contributes to stability: The rule and exceptions.

Science.gov (United States)

Khokhlova, T N; Kipnis, M M

2013-12-01

We prove that in our mathematical model the breaking of a delayed ring neural network extends the stability region in the parameters space, if the number of the neurons is sufficiently large. If the number of neurons is small, then a "paradoxical" region exists in the parameters space, wherein the ring neural configuration is stable, while the linear one is unstable. We study the conditions under which the paradoxical region is nonempty. We discuss how our mathematical modeling reflects neurosurgical operations with the severing of particular connections in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

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Xiangwei Bu

2017-01-01

Full Text Available In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

Improved Criteria on Delay-Dependent Stability for Discrete-Time Neural Networks with Interval Time-Varying Delays

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O. M. Kwon

2012-01-01

Full Text Available The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method.

Feedback stabilization of the axisymmetric instability of a deformable tokamak plasma

International Nuclear Information System (INIS)

Pomphrey, N.; Jardin, S.C.

1987-09-01

We analyze the magnetohydrodynamic (MHD) stability of the axisymmetric system consisting of a free boundary, non-circular cross-section tokamak plasma, finite resistivity passive conductors, and an active feedback system with magnetic flux pickup loops, a proportional amplifier with gain G, and current carrying poloidal field coils. Numerical simulation of a system that is unstable with G = 0 shows that for some placements of the pickup loops, the system will remain unstable for all values of G, while for other placements of the loops, the system will be stable for G > G/sub crit/. This behavior is explained by analysis using an extended energy principle, and it is shown to result from the deformability of the plasma cross section. 9 refs., 5 figs

Artificial neural networks versus conventional methods for boiling water reactor stability monitoring

International Nuclear Information System (INIS)

Hagen, T.H.J.J. van der

1995-01-01

The application of an artificial neural network (ANN) for boiling water reactor (BWR) stability monitoring was studied. A three-layer perceptron was trained on synthetic autocorrelation functions to estimate the decay ratio and the resonance frequency from measured neutron noise. Training of the ANN was improved by adding noise to the training patterns and by applying nonconventional error definitions in the generalized delta rule. The performance of the developed ANN was compared with those of conventional stability monitoring techniques. Explicit care was taken for generating unbiased test data. It is found that the trained ANN is capable of monitoring the stability of the Dodewaard BWR for four specific cases. By comparing properties such as the false alarm ratio, the alarm failure ratio, and the average time to alarm, it is shown that it performs worse than model-based methods in stability monitoring of exact second-order systems but that it is more robust (better resistant to corruptions of the input data and to deviations of the system at issue from an exact second-order system) than other methods. The latter explains its good performance on the Dodewaard BWR and is promising for the application of an ANN for stability monitoring of other reactors and for other operating conditions

The Endogenous Feedback Network

DEFF Research Database (Denmark)

Augustenborg, Claudia Carrara

2010-01-01

proposals, it will first be considered the extents of their reciprocal compatibility, tentatively shaping an integrated, theoretical profile of consciousness. A new theory, the Endogenous Feedback Network (EFN) will consequently be introduced which, beside being able to accommodate the main tenets...... of the reviewed theories, appears able to compensate for the explanatory gaps they leave behind. The EFN proposes consciousness as the phenomenon emerging from a distinct network of neural paths broadcasting the neural changes associated to any mental process. It additionally argues for the need to include a 5th...

Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

Science.gov (United States)

Wang, Dongshu; Huang, Lihong

2014-03-01

In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.

Nonlinear Output Feedback Control of Underwater Vehicle Propellers using Advance Speed Feedback

DEFF Research Database (Denmark)

Fossen, T.I.; Blanke, M.

1999-01-01

More accurate propeller shaft speed controllers can be designed by using nonlinear control theory. In this paper, an output feedback controller reconstructing the advance speed (speed of water going into the propeller) from vehicle speed measurements is derived. For this purpose a three-state model...... minimizes thruster losses due to variations in propeller axial inlet flow which is a major problem when applying conventional vehicle-propeller control systems. The proposed controller is simulated for an underwater vehicle equipped with a single propeller. From the simulations it can be concluded...... of propeller shaft speed, forward (surge) speed of the vehicle and axial inlet flow of the propeller is applied. A nonlinear observer in combination with an output feedback integral controller are derived by applying Lyapunov stability theory and exponential stability is proven. The output feedback controller...

Beam closed orbit feedback based on PID control

International Nuclear Information System (INIS)

Xuan Ke; Wang Lin; Liu Gongfa; Li Weimin; Li Chuan; Wang Jigang; Bao Xun; Xu Hongliang

2013-01-01

The algorithm in the feedback system has important influence on the performance of the beam orbit. Good feedback algorithm can greatly improve the beam orbit stability. In this paper, the theory of beam closed orbit correction, the principle of PID control and the beam closed orbit feedback correction using PID control were introduced. The simulation results were given. Compared with least-square method, the PID feedback algorithm makes the steady-state error smaller and more accurate, and enhances the beam orbit stability. (authors)

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity

DEFF Research Database (Denmark)

Fossen, T. I.; Blanke, Mogens

2000-01-01

Accurate propeller shaft speed controllers can be designed by using nonlinear control theory and feedback from the axial water velocity in the propeller disc. In this paper, an output feedback controller is derived, reconstructing the axial flow velocity from vehicle speed measurements, using...... a three-state model of propeller shaft speed, forward (surge) speed of the vehicle, and the axial flow velocity. Lyapunov stability theory is used to prove that a nonlinear observer combined with an output feedback integral controller provide exponential stability. The output feedback controller...... compensates for variations in thrust due to time variations in advance speed. This is a major problem when applying conventional vehicle-propeller control systems, The proposed controller is simulated for an underwater vehicle equipped with a single propeller. The simulations demonstrate that the axial water...

Stability region for a prompt power variation of a coupled-core system with positive prompt feedback

International Nuclear Information System (INIS)

Watanabe, S.; Nishina, K.

1984-01-01

A stability analysis using a one-group model is presented for a coupled-core system. Positive prompt feedback of a γp /SUB j/ form is assumed, where p /SUB j/ is the fractional power variation of core j. Prompt power variations over a range of a few milliseconds after a disturbance are analyzed. The analysis combines Lapunov's method, prompt jump approximation, and the eigenfunction expansion of coupling region response flux. The last is treated as a pseudo-delayed neutron precursor. An asymptotic stability region is found for p /SUB j/. For an asymmetric flux variation over a system of two coupled cores, either p /SUB I/ or p /SUB II/ can slightly exceed, by virtue of the coupling effect, the critical value (β/γ-1) of a single-core case. Such a stability region is increased by additional inclusion of the coupling region fundamental mode in the treatment. The coupling region contributes to stability through its delayed response and coupling. An optimum core separation distance for stability is found

Models of vocal learning in the songbird: Historical frameworks and the stabilizing critic.

Science.gov (United States)

Nick, Teresa A

2015-10-01

Birdsong is a form of sensorimotor learning that involves a mirror-like system that activates with both song hearing and production. Early models of song learning, based on behavioral measures, identified key features of vocal plasticity, such as the requirements for memorization of a tutor song and auditory feedback during song practice. The concept of a comparator, which compares the memory of the tutor song to auditory feedback, featured prominently. Later models focused on linking anatomically-defined neural modules to behavioral concepts, such as the comparator. Exploiting the anatomical modularity of the songbird brain, localized lesions illuminated mechanisms of the neural song system. More recent models have integrated neuronal mechanisms identified in other systems with observations in songbirds. While these models explain multiple aspects of song learning, they must incorporate computational elements based on unknown biological mechanisms to bridge the motor-to-sensory delay and/or transform motor signals into the sensory domain. Here, I introduce the stabilizing critic hypothesis, which enables sensorimotor learning by (1) placing a purely sensory comparator afferent of the song system and (2) endowing song system disinhibitory interneuron networks with the capacity both to bridge the motor-sensory delay through prolonged bursting and to stabilize song segments selectively based on the comparator signal. These proposed networks stabilize an otherwise variable signal generated by both putative mirror neurons and a cortical-basal ganglia-thalamic loop. This stabilized signal then temporally converges with a matched premotor signal in the efferent song motor cortex, promoting spike-timing-dependent plasticity in the premotor circuitry and behavioral song learning. © 2014 Wiley Periodicals, Inc.

Neural Correlates of Feedback Processing in Decision Making under Risk

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Beate eSchuermann

2012-07-01

Full Text Available Introduction. Event-related brain potentials (ERP provide important information about the sensitivity of the brain to process varying risks. The aim of the present study was to determine how different risk levels are reflected in decision-related ERPs, namely the feedback-related negativity (FRN and the P300. Material and Methods. 20 participants conducted a probabilistic two-choice gambling task while an electroencephalogram was recorded. Choices were provided between a low-risk option yielding low rewards and low losses and a high-risk option yielding high rewards and high losses. While options differed in expected risks, they were equal in expected values and in feedback probabilities. Results. At the behavioral level, participants were generally risk-averse but modulated their risk-taking behavior according to reward history. An early positivity (P200 was enhanced on negative feedbacks in high-risk compared to low-risk options. With regard to the FRN, there were significant amplitude differences between positive and negative feedbacks in high-risk options, but not in low-risk options. While the FRN on negative feedbacks did not vary with decision riskiness, reduced amplitudes were found for positive feedbacks in high-risk relative to low-risk choices. P300 amplitudes were larger in high-risk decisions, and in an additive way, after negative compared to positive feedback. Discussion. The present study revealed significant influences of risk and valence processing on ERPs. FRN findings suggest that the reward prediction error signal is increased after high-risk decisions. The increased P200 on negative feedback in risky decisions suggests that large negative prediction errors are processed as early as in the P200 time range. The later P300 amplitude is sensitive to feedback valence as well as to the risk of a decision. Thus, the P300 carries additional information for reward processing, mainly the enhanced motivational significance of risky

Computational aspects of feedback in neural circuits.

Directory of Open Access Journals (Sweden)

Wolfgang Maass

2007-01-01

Full Text Available It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also

Sensory feedback in upper limb prosthetics.

Science.gov (United States)

Antfolk, Christian; D'Alonzo, Marco; Rosén, Birgitta; Lundborg, Göran; Sebelius, Fredrik; Cipriani, Christian

2013-01-01

One of the challenges facing prosthetic designers and engineers is to restore the missing sensory function inherit to hand amputation. Several different techniques can be employed to provide amputees with sensory feedback: sensory substitution methods where the recorded stimulus is not only transferred to the amputee, but also translated to a different modality (modality-matched feedback), which transfers the stimulus without translation and direct neural stimulation, which interacts directly with peripheral afferent nerves. This paper presents an overview of the principal works and devices employed to provide upper limb amputees with sensory feedback. The focus is on sensory substitution and modality matched feedback; the principal features, advantages and disadvantages of the different methods are presented.

Homogeneous Stabilizer by State Feedback for Switched Nonlinear Systems Using Multiple Lyapunov Functions’ Approach

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Hui Ye

2017-01-01

Full Text Available This paper investigates the problem of global stabilization for a class of switched nonlinear systems using multiple Lyapunov functions (MLFs. The restrictions on nonlinearities are neither linear growth condition nor Lipschitz condition with respect to system states. Based on adding a power integrator technique, we design homogeneous state feedback controllers of all subsystems and a switching law to guarantee that the closed-loop system is globally asymptotically stable. Finally, an example is given to illustrate the validity of the proposed control scheme.

A gimbal platform stabilization for topographic applications

Energy Technology Data Exchange (ETDEWEB)

Michele, Mangiameli, E-mail: michele.mangiameli@dica.unict.it; Giuseppe, Mussumeci [Dept. of Civil Engineering and Architecture, University of Catania, Catania (Italy)

2015-03-10

The aim of this work is the stabilization of a Gimbal platform for optical sensors acquisitions in topographic applications using mobile vehicles. The stabilization of the line of sight (LOS) consists in tracking the command velocity in presence of nonlinear noise due to the external environment. The hardware architecture is characterized by an Ardupilot platform that allows the control of both the mobile device and the Gimbal. Here we developed a new approach to stabilize the Gimbal platform, which is based on neural network. For the control system, we considered a plant that represents the transfer function of the servo system control model for an inertial stabilized Gimbal platform. The transductor used in the feed-back line control is characterized by the Rate Gyro transfer function installed onboard of Ardupilot. For the simulation and investigation of the system performance, we used the Simulink tool of Matlab. Results show that the hardware/software approach is efficient, reliable and cheap for direct photogrammetry, as well as for general purpose applications using mobile vehicles.

Adaptive Fuzzy Output-Feedback Method Applied to Fin Control for Time-Delay Ship Roll Stabilization

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Rui Bai

2014-01-01

Full Text Available The ship roll stabilization by fin control system is considered in this paper. Assuming that angular velocity in roll cannot be measured, an adaptive fuzzy output-feedback control is investigated. The fuzzy logic system is used to approximate the uncertain term of the controlled system, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the fuzzy state observer and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB, and the control strategy is effective to decrease the roll motion. Simulation results are included to illustrate the effectiveness of the proposed approach.

Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stability.

Science.gov (United States)

Barroso, L M A; Teodoro, P E; Nascimento, M; Torres, F E; Nascimento, A C C; Azevedo, C F; Teixeira, F R F

2016-11-03

Cowpea (Vigna unguiculata) is grown in three Brazilian regions: the Midwest, North, and Northeast, and is consumed by people on low incomes. It is important to investigate the genotype x environment (GE) interaction to provide accurate recommendations for farmers. The aim of this study was to identify cowpea genotypes with high adaptability and phenotypic stability for growing in the Brazilian Cerrado, and to compare the use of artificial neural networks with the Eberhart and Russell (1966) method. Six trials with upright cowpea genotypes were conducted in 2005 and 2006 in the States of Mato Grosso do Sul and Mato Grosso. The data were subjected to adaptability and stability analysis by the Eberhart and Russell (1966) method and artificial neural networks. The genotypes MNC99-537F-4 and EVX91-2E-2 provided grain yields above the overall environment means, and exhibited high stability according to both methods. Genotype IT93K-93-10 was the most suitable for unfavorable environments. There was a high correlation between the results of both methods in terms of classifying the genotypes by their adaptability and stability. Therefore, this new approach would be effective in quantifying the GE interaction in upright cowpea breeding programs.

Additive Feed Forward Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1999-01-01

This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

Global stability of almost periodic solution of shunting inhibitory cellular neural networks with variable coefficients

International Nuclear Information System (INIS)

Chen Ling; Zhao Hongyong

2008-01-01

The paper investigates the almost periodicity of shunting inhibitory cellular neural networks with delays and variable coefficients. Several sufficient conditions are established for the existence and globally exponential stability of almost periodic solutions by employing fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results

Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

Directory of Open Access Journals (Sweden)

Emmanuel García

2014-01-01

Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

Delay-dependent stability of neural networks of neutral type with time delay in the leakage term

International Nuclear Information System (INIS)

Li, Xiaodi; Cao, Jinde

2010-01-01

This paper studies the global asymptotic stability of neural networks of neutral type with mixed delays. The mixed delays include constant delay in the leakage term (i.e. 'leakage delay'), time-varying delays and continuously distributed delays. Based on the topological degree theory, Lyapunov method and linear matrix inequality (LMI) approach, some sufficient conditions are derived ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point, which are dependent on both the discrete and distributed time delays. These conditions are expressed in terms of LMI and can be easily checked by the MATLAB LMI toolbox. Even if there is no leakage delay, the obtained results are less restrictive than some recent works. It can be applied to neural networks of neutral type with activation functions without assuming their boundedness, monotonicity or differentiability. Moreover, the differentiability of the time-varying delay in the non-neutral term is removed. Finally, two numerical examples are given to show the effectiveness of the proposed method

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

Directory of Open Access Journals (Sweden)

Eyad K Almaita

2017-03-01

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

Stability in Cohen Grossberg-type bidirectional associative memory neural networks with time-varying delays

Science.gov (United States)

Cao, Jinde; Song, Qiankun

2006-07-01

In this paper, the exponential stability problem is investigated for a class of Cohen-Grossberg-type bidirectional associative memory neural networks with time-varying delays. By using the analysis method, inequality technique and the properties of an M-matrix, several novel sufficient conditions ensuring the existence, uniqueness and global exponential stability of the equilibrium point are derived. Moreover, the exponential convergence rate is estimated. The obtained results are less restrictive than those given in the earlier literature, and the boundedness and differentiability of the activation functions and differentiability of the time-varying delays are removed. Two examples with their simulations are given to show the effectiveness of the obtained results.

Mobile robot nonlinear feedback control based on Elman neural network observer

Directory of Open Access Journals (Sweden)

Khaled Al-Mutib

2015-12-01

Full Text Available This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

Controlling the unstable emission of a semiconductor laser subject to conventional optical feedback with a filtered feedback branch.

Science.gov (United States)

Ermakov, I V; Tronciu, V Z; Colet, Pere; Mirasso, Claudio R

2009-05-25

We show the advantages of controlling the unstable dynamics of a semiconductor laser subject to conventional optical feedback by means of a second filtered feedback branch. We give an overview of the analytical solutions of the double cavity feedback and show numerically that the region of stabilization is much larger when using a second branch with filtered feedback than when using a conventional feedback one.

Controlling the unstable emission of a semiconductor laser subject to conventional optical feedback with a filtered feedback branch

OpenAIRE

Ermakov, Ilya; Tronciu, Vasile; Colet, Pere; Mirasso, Claudio R.

2009-01-01

We show the advantages of controlling the unstable dynamics of a semiconductor laser subject to conventional optical feedback by means of a second filtered feedback branch. We give an overview of the analytical solutions of the double cavity feedback and show numerically that the region of stabilization is much larger when using a second branch with filtered feedback than when using a conventional feedback one.

Analytic modeling of the feedback stabilization of resistive wall modes

International Nuclear Information System (INIS)

Pustovitov, Vladimir D.

2003-01-01

Feedback suppression of resistive wall modes (RWM) is studied analytically using a model based on a standard cylindrical approximation. Optimal choice of the input signal for the feedback, effects related to the geometry of the feedback active coils, RWM suppression in a configuration with ITER-like double wall, are considered here. The widespread opinion that the feedback with poloidal sensors is better than that with radial sensors is discussed. It is shown that for an ideal feedback system the best input signal would be a combination of radial and poloidal perturbations measured inside the vessel. (author)

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

International Nuclear Information System (INIS)

Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

2012-01-01

The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

Truncated predictor feedback for time-delay systems

CERN Document Server

Zhou, Bin

2014-01-01

This book provides a systematic approach to the design of predictor based controllers for (time-varying) linear systems with either (time-varying) input or state delays. Differently from those traditional predictor based controllers, which are infinite-dimensional static feedback laws and may cause difficulties in their practical implementation, this book develops a truncated predictor feedback (TPF) which involves only finite dimensional static state feedback. Features and topics: A novel approach referred to as truncated predictor feedback for the stabilization of (time-varying) time-delay systems in both the continuous-time setting and the discrete-time setting is built systematically Semi-global and global stabilization problems of linear time-delay systems subject to either magnitude saturation or energy constraints are solved in a systematic manner Both stabilization of a single system and consensus of a group of systems (multi-agent systems) are treated in a unified manner by applying the truncated pre...

Coordinated three-dimensional motion of the head and torso by dynamic neural networks.

Science.gov (United States)

Kim, J; Hemami, H

1998-01-01

The problem of trajectory tracking control of a three dimensional (3D) model of the human upper torso and head is considered. The torso and the head are modeled as two rigid bodies connected at one point, and the Newton-Euler method is used to derive the nonlinear differential equations that govern the motion of the system. The two-link system is driven by six pairs of muscle like actuators that possess physiologically inspired alpha like and gamma like inputs, and spindle like and Golgi tendon organ like outputs. These outputs are utilized as reflex feedback for stability and stiffness control, in a long loop feedback for the purpose of estimating the state of the system (somesthesis), and as part of the input to the controller. Ideal delays of different duration are included in the feedforward and feedback paths of the system to emulate such delays encountered in physiological systems. Dynamical neural networks are trained to learn effective control of the desired maneuvers of the system. The feasibility of the controller is demonstrated by computer simulation of the successful execution of the desired maneuvers. This work demonstrates the capabilities of neural circuits in controlling highly nonlinear systems with multidelays in their feedforward and feedback paths. The ultimate long range goal of this research is toward understanding the working of the central nervous system in controlling movement. It is an interdisciplinary effort relying on mechanics, biomechanics, neuroscience, system theory, physiology and anatomy, and its short range relevance to rehabilitation must be noted.

A simulation study of the global orbit feedback system for Pohang light source

International Nuclear Information System (INIS)

Kim, Kukhee; Shim, Kyuyeol; Cho, Moohyun; Namkung, Won; Ko, In Soo; Choi, Jinhyuk

2000-01-01

This paper describes the simulation of the global orbit feedback system using the singular value decomposition (SVD) method, the error minimization method, and the neural network method. Instead of facing unacceptable correction result raised occasionally in the SVD method, we choose the error minimization method for the global orbit feedback. This method provides minimum orbit errors while avoiding unacceptable corrections, and keeps the orbit within the dynamic aperture of the storage ring. We simulate the Pohang Light Source (PLS) storage ring using the Methodical Accelerator Design (MAD) code that generates the orbit distortions for the error minimization method and the learning data set for neural network method. In order to compare the effectiveness of the neural network method with others, a neural network is trained by the learning algorithm using the learning data set. The global response matrix with a minimum error and the trained neural network are used to the global orbit feedback system. The simulation shows that a selection of beam position monitors (BPMs) is very sensitive in the reduction of rms orbit distortions, and the random choice gives better results than any other cases. (author)

Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

Directory of Open Access Journals (Sweden)

Minoru Sasaki

2012-01-01

Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays.

Science.gov (United States)

Li, Hongfei; Jiang, Haijun; Hu, Cheng

2016-03-01

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

neural control system

International Nuclear Information System (INIS)

Elshazly, A.A.E.

2002-01-01

Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

Synchronized stability in a reaction–diffusion neural network model

Energy Technology Data Exchange (ETDEWEB)

Wang, Ling; Zhao, Hongyong, E-mail: hongyongz@126.com

2014-11-14

The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.

Synchronized stability in a reaction–diffusion neural network model

International Nuclear Information System (INIS)

Wang, Ling; Zhao, Hongyong

2014-01-01

The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability

Effects of kinesthetic haptic feedback on standing stability of young healthy subjects and stroke patients.

Science.gov (United States)

Afzal, Muhammad Raheel; Byun, Ha-Young; Oh, Min-Kyun; Yoon, Jungwon

2015-03-13

Haptic control is a useful therapeutic option in rehabilitation featuring virtual reality interaction. As with visual and vibrotactile biofeedback, kinesthetic haptic feedback may assist in postural control, and can achieve balance control. Kinesthetic haptic feedback in terms of body sway can be delivered via a commercially available haptic device and can enhance the balance stability of both young healthy subjects and stroke patients. Our system features a waist-attached smartphone, software running on a computer (PC), and a dedicated Phantom Omni® device. Young healthy participants performed balance tasks after assumption of each of four distinct postures for 30 s (one foot on the ground; the Tandem Romberg stance; one foot on foam; and the Tandem Romberg stance on foam) with eyes closed. Patient eyes were not closed and assumption of the Romberg stance (only) was tested during a balance task 25 s in duration. An Android application running continuously on the smartphone sent mediolateral (ML) and anteroposterior (AP) tilt angles to a PC, which generated kinesthetic haptic feedback via Phantom Omni®. A total of 16 subjects, 8 of whom were young healthy and 8 of whom had suffered stroke, participated in the study. Post-experiment data analysis was performed using MATLAB®. Mean Velocity Displacement (MVD), Planar Deviation (PD), Mediolateral Trajectory (MLT) and Anteroposterior Trajectory (APT) parameters were analyzed to measure reduction in body sway. Our kinesthetic haptic feedback system was effective to reduce postural sway in young healthy subjects regardless of posture and the condition of the substrate (the ground) and to improve MVD and PD in stroke patients who assumed the Romberg stance. Analysis of Variance (ANOVA) revealed that kinesthetic haptic feedback significantly reduced body sway in both categories of subjects. Kinesthetic haptic feedback can be implemented using a commercial haptic device and a smartphone. Intuitive balance cues were

An artificial neural network approach to reconstruct the source term of a nuclear accident

International Nuclear Information System (INIS)

Giles, J.; Palma, C. R.; Weller, P.

1997-01-01

This work makes use of one of the main features of artificial neural networks, which is their ability to 'learn' from sets of known input and output data. Indeed, a trained artificial neural network can be used to make predictions on the input data when the output is known, and this feedback process enables one to reconstruct the source term from field observations. With this aim, an artificial neural networks has been trained, using the projections of a segmented plume atmospheric dispersion model at fixed points, simulating a set of gamma detectors located outside the perimeter of a nuclear facility. The resulting set of artificial neural networks was used to determine the release fraction and rate for each of the noble gases, iodines and particulate fission products that could originate from a nuclear accident. Model projections were made using a large data set consisting of effective release height, release fraction of noble gases, iodines and particulate fission products, atmospheric stability, wind speed and wind direction. The model computed nuclide-specific gamma dose rates. The locations of the detectors were chosen taking into account both building shine and wake effects, and varied in distance between 800 and 1200 m from the reactor.The inputs to the artificial neural networks consisted of the measurements from the detector array, atmospheric stability, wind speed and wind direction; the outputs comprised a set of release fractions and heights. Once trained, the artificial neural networks was used to reconstruct the source term from the detector responses for data sets not used in training. The preliminary results are encouraging and show that the noble gases and particulate fission product release fractions are well determined

Nolinear stability analysis of nuclear reactors : expansion methods for stability domains

International Nuclear Information System (INIS)

Yang, Chae Yong

1992-02-01

Two constructive methods for estimating asymptotic stability domains of nonlinear reactor models are developed in this study: an improved Chang and Thorp's method based on expansion of a Lyapunov function and a new method based on expansion of any positive definite function. The methods are established on the concept of stability definitions of Lyapunov itself. The first method provides a sequence of stability regions that eventually approaches the exact stability domain, but requires many expansions in order to obtain the entire stability region because the starting Lyapunov function usually corresponds to a small stability region and because most dynamic systems are stiff. The second method (new method) requires only a positive definite function and thus it is easy to come up with a starting region. From a large starting region, the entire stability region is estimated effectively after sufficient iterations. It is particularly useful for stiff systems. The methods are applied to several nonlinear reactor models known in the literature: one-temperature feedback model, two-temperature feedback model, and xenon dynamics model, and the results are compared. A reactor feedback model for a pressurized water reactor (PWR) considering fuel and moderator temperature effects is developed and the nonlinear stability regions are estimated for the various values of design parameters by using the new method. The steady-state properties of the nonlinear reactor system are analyzed via bifurcation theory. The analysis of nonlinear phenomena is carried out for the various forms of reactivity feedback coefficients that are both temperature- (or power-) independent and dependent. If one of two temperature coefficients is positive, unstable limit cycles or multiplicity of the steady-state solutions appear when the other temperature coefficient exceeds a certain critical value. As an example, even though the fuel temperature coefficient is negative, if the moderator temperature

Offshore Wind Farms and HVDC Grids Modeling as a Feedback Control System for Stability Analysis

DEFF Research Database (Denmark)

Bidadfar, Ali; Saborío-Romano, Oscar; Altin, Müfit

The low impedance characteristics of DC transmission lines cause the voltage source converter (VSC) in HVDC networks to become electrically closer together and increase the risk of severe interactions between the converters. Such interactions, in turn, intensify the implementation of the grid...... control schemes and may lead the entire system to instability. Assessing the stability and adopting complex coordinated control schemes in an HVDC grid and wind farm turbines are challenging and require a precise model of the HVDC grid, wind farm, and the controllers. In this paper, a linear multivariable...... feedback control system (FCS) model is proposed to represent the dynamic characteristics of HVDC grids and their controllers. The FCS model can be used for different dynamic analyses in time and frequency domains. Moreover, using the FCS model the system stability is analyzed in both open- and closed...

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

Science.gov (United States)

Xu, Chang-Jin; Li, Pei-Luan; Pang, Yi-Cheng

2017-02-01

This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results. Supported by National Natural Science Foundation of China under Grant Nos.~61673008, 11261010, 11101126, Project of High-Level Innovative Talents of Guizhou Province ([2016]5651), Natural Science and Technology Foundation of Guizhou Province (J[2015]2025 and J[2015]2026), 125 Special Major Science and Technology of Department of Education of Guizhou Province ([2012]011) and Natural Science Foundation of the Education Department of Guizhou Province (KY[2015]482)

Direct adaptive control using feedforward neural networks

OpenAIRE

Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

2003-01-01

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

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

Science.gov (United States)

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

2003-01-01

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

Globally Asymptotic Stability of Stochastic Nonlinear Systems with Time-Varying Delays via Output Feedback Control

Directory of Open Access Journals (Sweden)

Mingzhu Song

2016-01-01

Full Text Available We address the problem of globally asymptotic stability for a class of stochastic nonlinear systems with time-varying delays. By the backstepping method and Lyapunov theory, we design a linear output feedback controller recursively based on the observable linearization for a class of stochastic nonlinear systems with time-varying delays to guarantee that the closed-loop system is globally asymptotically stable in probability. In particular, we extend the deterministic nonlinear system to stochastic nonlinear systems with time-varying delays. Finally, an example and its simulations are given to illustrate the theoretical results.

Temperature feedback control for long-term carrier-envelope phase locking

Science.gov (United States)

Chang, Zenghu [Manhattan, KS; Yun, Chenxia [Manhattan, KS; Chen, Shouyuan [Manhattan, KS; Wang, He [Manhattan, KS; Chini, Michael [Manhattan, KS

2012-07-24

A feedback control module for stabilizing a carrier-envelope phase of an output of a laser oscillator system comprises a first photodetector, a second photodetector, a phase stabilizer, an optical modulator, and a thermal control element. The first photodetector may generate a first feedback signal corresponding to a first portion of a laser beam from an oscillator. The second photodetector may generate a second feedback signal corresponding to a second portion of the laser beam filtered by a low-pass filter. The phase stabilizer may divide the frequency of the first feedback signal by a factor and generate an error signal corresponding to the difference between the frequency-divided first feedback signal and the second feedback signal. The optical modulator may modulate the laser beam within the oscillator corresponding to the error signal. The thermal control unit may change the temperature of the oscillator corresponding to a signal operable to control the optical modulator.

Real-time orbit feedback at the APS

International Nuclear Information System (INIS)

Carwardine, J.

1998-01-01

A real-time orbit feedback system has been implemented at the Advanced Photon Source in order to meet the stringent orbit stability requirements. The system reduces global orbit motion below 30Hz by a factor of four to below 5 microm rms horizontally and 2 microm rms vertically. This paper focuses on dynamic orbit stability and describes the all-digital orbit feedback system that has been implemented at the APS. Implementation of the global orbit feedback system is described and its latest performance is presented. Ultimately, the system will provide local feedback at each x-ray source point using installed photon BPMs to measure x-ray beam position and angle directly. Technical challenges associated with local feedback and with dynamics of the associated corrector magnets are described. The unique diagnostic capabilities provided by the APS system are discussed with reference to their use in identifying sources of the underlying orbit motion

Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

International Nuclear Information System (INIS)

Liu, Yurong; Wang, Zidong; Serrano, Alan; Liu, Xiaohui

2007-01-01

This Letter is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a Lyapunov-Krasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition

The neural sociometer: brain mechanisms underlying state self-esteem.

Science.gov (United States)

Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

2011-11-01

On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

Feedback-type giant magneto-impedance sensor based on longitudinal excitation

International Nuclear Information System (INIS)

Zhao Wen; Bu Xiongzhu; Yu Geliang; Xiang Chao

2012-01-01

In this paper, the characteristics of Fe-based amorphous ribbon based on the longitudinal excitation are investigated with assistance of the theory of the giant magneto-impedance (GMI) effect. A feedback-type GMI micro-magnetic sensor is designed with regard to the design of the sensing element, the excitation circuit, the conditional circuit and the feedback circuit. With the analysis of the dynamic and static characteristics of the feedback-type GMI sensor, it is concluded that the designed feedback-type GMI sensor has higher linearity, stability and dynamic characteristics than non-feedback-type GMI sensor in −2.5 to +2.5 Oe. - Highlights: ► A feedback-type GMI micro-magnetic sensor is designed. ► Excitation coil and feedback coil of the sensor is designed to be in one. ► The feedback-type sensor has higher linearity and stability than non-feedback type. ► The feedback-type sensor has better dynamic characteristics than non-feedback type.

Feedback stabilization of MHD instabilities. Report on the Workshop held at Princeton Plasma Physics Lab., Princeton Univ., Princeton, New Jersey, United States of America, 11-13 December 1996

International Nuclear Information System (INIS)

McGuire, K.M.; Kugel, H.W.; La Haye, R.J.; Mauel, M.E.; Nevins, W.M.; Prager, S.C.

1997-01-01

The transient operating performance of magnetic confinement devices is often limited by one or two unstable MHD modes. The feedback stabilization of MHD instabilities is an area of research that is critical for improving the steady state performance and economic attractiveness of magnetic confinement devices. This growing realization motivated a Workshop dedicated to feedback stabilization of MHD instabilities, which was held from 11 to 13 December 1996 at Princeton Plasma Physics Laboratory. The resulting presentations, conclusions and recommendations are summarized. (author)

The neural representation of typical and atypical experiences of negative images: comparing fear, disgust and morbid fascination

Science.gov (United States)

Lindquist, Kristen A.; Adebayo, Morenikeji; Barrett, Lisa Feldman

2016-01-01

Negative stimuli do not only evoke fear or disgust, but can also evoke a state of ‘morbid fascination’ which is an urge to approach and explore a negative stimulus. In the present neuroimaging study, we applied an innovative method to investigate the neural systems involved in typical and atypical conceptualizations of negative images. Participants received false feedback labeling their mental experience as fear, disgust or morbid fascination. This manipulation was successful; participants judged the false feedback correct for 70% of the trials on average. The neuroimaging results demonstrated differential activity within regions in the ‘neural reference space for discrete emotion’ depending on the type of feedback. We found robust differences in the ventrolateral prefrontal cortex, the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex comparing morbid fascination to control feedback. More subtle differences in the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex were also found between morbid fascination feedback and the other emotion feedback conditions. This study is the first to forward evidence about the neural representation of the experimentally unexplored state of morbid fascination. In line with a constructionist framework, our findings suggest that neural resources associated with the process of conceptualization contribute to the neural representation of this state. PMID:26180088

Global Stability in Dynamical Systems with Multiple Feedback Mechanisms

DEFF Research Database (Denmark)

Andersen, Morten; Vinther, Frank; Ottesen, Johnny T.

2016-01-01

A class of n-dimensional ODEs with up to n feedbacks from the n’th variable is analysed. The feedbacks are represented by non-specific, bounded, non-negative C1 functions. The main result is the formulation and proof of an easily applicable criterion for existence of a globally stable fixed point...

Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix

International Nuclear Information System (INIS)

Singh, Vimal

2007-01-01

The question of estimating the upper limit of -parallel B -parallel 2 , which is a key step in some recently reported global robust stability criteria for delayed neural networks, is revisited ( B denotes the delayed connection weight matrix). Recently, Cao, Huang, and Qu have given an estimate of the upper limit of -parallel B -parallel 2 . In the present paper, an alternative estimate of the upper limit of -parallel B -parallel 2 is highlighted. It is shown that the alternative estimate may yield some new global robust stability results

Global Control of the Furuta Pendulum Using Artificial Neural Networks and Feedback of State Variables

Directory of Open Access Journals (Sweden)

Luisa F. Escobar-Dávila

2013-06-01

Full Text Available This paper presents the mathematical modeling of the Furuta Pendu-lum by power functions, taking into account the non linear own dynamics of the physical systems and considering the existing couplings between the electric and mechanic devices. A control process based on feedback of state variables (FSV for the equilibrium point is developed and two topics for the non linear zone are addressed. First of all, functions are implemented to represent the energetic states of the plant in a global way and the operation regions are established (“Swing up” zone, and later Artificial Neural Networks (ANN are employed to simulate the behavior of the energy functions. Finally, it is presented the combination between the control techniques, considering the own constrains of the actuators and sensors used, besides of this, a study is done in a simulated environment of the physical phenomena that may disturb system behavior, and the capacity, sensitivity and robustness of the controller is verified.

Simultaneous Stabilization of Gyrotron Frequency and Power by PID Double Feedback Control on the Acceleration and Anode Voltages

Science.gov (United States)

Khutoryan, E. M.; Idehara, T.; Kuleshov, A. N.; Tatematsu, Y.; Yamaguchi, Y.; Matsuki, Y.; Fujiwara, T.

2017-07-01

In this paper, we present the results of simultaneous stabilization of both the frequency and the output power by a double PID feedback control on the acceleration and anode voltages in the 460-GHz gyrotron FU CW GVI, also known as "Gyrotron FU CW GO-1" (according to the nomenclature adopted at Osaka University). The approach used in the experiments is based on the modulation of the cyclotron frequency and the pitch factor (velocity ratio) of the electron beam by varying the acceleration and the anode voltages, respectively. In a long-term experiment, the frequency and power stabilities were made to be better than ±10-6 and ±1%, respectively.

Fast feedback for linear colliders

International Nuclear Information System (INIS)

Hendrickson, L.; Adolphsen, C.; Allison, S.; Gromme, T.; Grossberg, P.; Himel, T.; Krauter, K.; MacKenzie, R.; Minty, M.; Sass, R.

1995-01-01

A fast feedback system provides beam stabilization for the SLC. As the SLC is in some sense a prototype for future linear colliders, this system may be a prototype for future feedbacks. The SLC provides a good base of experience for feedback requirements and capabilities as well as a testing ground for performance characteristics. The feedback system controls a wide variety of machine parameters throughout the SLC and associated experiments, including regulation of beam position, angle, energy, intensity and timing parameters. The design and applications of the system are described, in addition to results of recent performance studies

Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

Science.gov (United States)

Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

2017-10-01

This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

Generalized fast feedback system in the SLC

International Nuclear Information System (INIS)

Hendrickson, L.; Allison, S.; Gromme, T.; Himel, T.; Krauter, K.; Rouse, F.; Sass, R.; Shoaee, H.

1991-11-01

A generalized fast feedback system has been developed to stabilize beams at various locations in the SLC. The system is designed to perform measurements and change actuator settings to control beam states such as position, angle and energy on a pulse to pulse basis. The software design is based on the state space formalism of digital control theory. The system is database-driven, facilitating the addition of new loops without requiring additional software. A communications system, KISNet, provides fast communications links between microprocessors for feedback loops which involve multiple micros. Feedback loops have been installed in seventeen locations throughout the SLC and have proven to be invaluable in stabilizing the machine

Feedback Control of a Class of Nonholonomic Hamiltonian Systems

DEFF Research Database (Denmark)

Sørensen, Mathias Jesper

Feedback control of nonholonomic systems has always been problematic due to the nonholonomic constraints that limit the space of possible system velocities. This property is very basic, and Brockett proved that a nonholonomic system cannot be asymptotically stabilized by a time-invariant smooth...... turns out to be useful when stabilizing the nonholonomic system. If the system is properly actuated it is possible to asymptotically stabilize the primary part of the configuration coordinates via a passive energy shaping and damping injecting feedback. The feedback is smooth and time......-invariant, but since it does not asymptotically stabilize the secondary part of the configuration coordinates, it does not violate Brockett’s obstruction. The results fromthe general class of nonholonomicHamiltonian systems with kinematic inputs are applied to a real implementation of a four wheel steered, four wheel...

Neural set point for the control of arterial pressure: role of the nucleus tractus solitarius

Directory of Open Access Journals (Sweden)

Valentinuzzi Max E

2010-01-01

Full Text Available Abstract Background Physiological experiments have shown that the mean arterial blood pressure (MAP can not be regulated after chemo and cardiopulmonary receptor denervation. Neuro-physiological information suggests that the nucleus tractus solitarius (NTS is the only structure that receives information from its rostral neural nuclei and from the cardiovascular receptors and projects to nuclei that regulate the circulatory variables. Methods From a control theory perspective, to answer if the cardiovascular regulation has a set point, we should find out whether in the cardiovascular control there is something equivalent to a comparator evaluating the error signal (between the rostral projections to the NTS and the feedback inputs. The NTS would function as a comparator if: a its lesion suppresses cardiovascular regulation; b the negative feedback loop still responds normally to perturbations (such as mechanical or electrical after cutting the rostral afferent fibers to the NTS; c perturbation of rostral neural structures (RNS to the NTS modifies the set point without changing the dynamics of the elicited response; and d cardiovascular responses to perturbations on neural structures within the negative feedback loop compensate for much faster than perturbations on the NTS rostral structures. Results From the control theory framework, experimental evidence found currently in the literature plus experimental results from our group was put together showing that the above-mentioned conditions (to show that the NTS functions as a comparator are satisfied. Conclusions Physiological experiments suggest that long-term blood pressure is regulated by the nervous system. The NTS functions as a comparator (evaluating the error signal between its RNS and the cardiovascular receptor afferents and projects to nuclei that regulate the circulatory variables. The mean arterial pressure (MAP is regulated by the feedback of chemo and cardiopulmonary receptors and

Brain circuits underlying visual stability across eye movements - converging evidence for a neuro-computational model of area LIP

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Arnold eZiesche

2014-03-01

Full Text Available The understanding of the subjective experience of a visually stable world despite the occurrence of an observer's eye movements has been the focus of extensive research for over 20 years. These studies have revealed fundamental mechanisms such as anticipatory receptive field shifts and the saccadic suppression of stimulus displacements, yet there currently exists no single explanatory framework for these observations. We show that a previously presented neuro-computational model of peri-saccadic mislocalization accounts for the phenomenon of predictive remapping and for the observation of saccadic suppression of displacement (SSD. This converging evidence allows us to identify the potential ingredients of perceptual stability that generalize beyond different data sets in a formal physiology-based model. In particular we propose that predictive remapping stabilizes the visual world across saccades by introducing a feedback loop and, as an emergent result, small displacements of stimuli are not noticed by the visual system. The model provides a link from neural dynamics, to neural mechanism and finally to behavior, and thus offers a testable comprehensive framework of visual stability.

Real-time orbit feedback at the APS

International Nuclear Information System (INIS)

Carwardine, J.A.; Lenkszus, F.R.

1998-01-01

A real-time orbit feedback system has been implemented at the Advanced Photon Source in order to meet the stringent orbit stability requirements. The system reduces global orbit motion below 30 Hz by a factor of four to below 5 μm rms horizontally and 2 μm rms vertically. This paper focuses on dynamic orbit stability and describes the all-digital orbit feedback system that has been implemented at the APS. Implementation of the global orbit feedback system is described and its latest performance is presented. Ultimately, the system will provide local feedback at each x-ray source point using installed photon BPMs to measure x-ray beam position and angle directly. Technical challenges associated with local feedback and with dynamics of the associated corrector magnets are described. The unique diagnostic capabilities provided by the APS system are discussed with reference to their use in identifying sources of the underlying orbit motion. copyright 1998 American Institute of Physics

Real-time orbit feedback at the APS.

Energy Technology Data Exchange (ETDEWEB)

Carwardine, J.

1998-06-18

A real-time orbit feedback system has been implemented at the Advanced Photon Source in order to meet the stringent orbit stability requirements. The system reduces global orbit motion below 30Hz by a factor of four to below 5{micro}m rms horizontally and 2{micro}m rms vertically. This paper focuses on dynamic orbit stability and describes the all-digital orbit feedback system that has been implemented at the APS. Implementation of the global orbit feedback system is described and its latest performance is presented. Ultimately, the system will provide local feedback at each x-ray source point using installed photon BPMs to measure x-ray beam position and angle directly. Technical challenges associated with local feedback and with dynamics of the associated corrector magnets are described. The unique diagnostic capabilities provided by the APS system are discussed with reference to their use in identifying sources of the underlying orbit motion.

Stability Analysis of Continuous-Time and Discrete-Time Quaternion-Valued Neural Networks With Linear Threshold Neurons.

Science.gov (United States)

Chen, Xiaofeng; Song, Qiankun; Li, Zhongshan; Zhao, Zhenjiang; Liu, Yurong

2018-07-01

This paper addresses the problem of stability for continuous-time and discrete-time quaternion-valued neural networks (QVNNs) with linear threshold neurons. Applying the semidiscretization technique to the continuous-time QVNNs, the discrete-time analogs are obtained, which preserve the dynamical characteristics of their continuous-time counterparts. Via the plural decomposition method of quaternion, homeomorphic mapping theorem, as well as Lyapunov theorem, some sufficient conditions on the existence, uniqueness, and global asymptotical stability of the equilibrium point are derived for the continuous-time QVNNs and their discrete-time analogs, respectively. Furthermore, a uniform sufficient condition on the existence, uniqueness, and global asymptotical stability of the equilibrium point is obtained for both continuous-time QVNNs and their discrete-time version. Finally, two numerical examples are provided to substantiate the effectiveness of the proposed results.

Requirements for active resistive wall mode (RWM) feedback control

International Nuclear Information System (INIS)

In, Y; Kim, J S; Chu, M S; Jackson, G L; La Haye, R J; Strait, E J; Liu, Y Q; Marrelli, L; Okabayashi, M; Reimerdes, H

2010-01-01

The requirements for active resistive wall mode (RWM) feedback control have been systematically investigated and established using highly reproducible current-driven RWMs in ohmic discharges in DIII-D. The unambiguous evaluation of active RWM feedback control was not possible in previous RWM studies primarily due to the variability of the onset of the pressure-driven RWMs; the stability of the pressure-driven RWM is thought to be sensitive to various passive stabilization mechanisms. Both feedback control specifications and physics requirements for RWM stabilization have been clarified using the current-driven RWMs in ohmic discharges, when little or no passive stabilization effects are present. The use of derivative gain on top of proportional gain is found to be advantageous. An effective feedback control system should be equipped with a power supply with bandwidth greater than the RWM growth rate. It is beneficial to apply a feedback field that is toroidally phase-shifted from the measured RWM phase in the same direction as the plasma current. The efficacy of the RWM feedback control will ultimately be determined by the plasma fluctuations on internal diagnostics, as well as on external magnetics. The proximity of the feedback coils to the plasma appears to be an important factor in determining the effectiveness of the RWM feedback coils. It is desirable that an RWM feedback control system simultaneously handles error field correction at a low frequency, along with direct RWM feedback at a high frequency. There is an indication of the influence of a second least stable RWM, which had been theoretically predicted but never identified in experiments. A preliminary investigation based on active MHD spectroscopic measurement showed a strong plasma response around 400 Hz where the typical plasma response associated with the first least stable RWM was expected to be negligible. Present active feedback control requirements are based on a single mode assumption, so the

Positivity effect in healthy aging in observational but not active feedback-learning.

Science.gov (United States)

Bellebaum, Christian; Rustemeier, Martina; Daum, Irene

2012-01-01

The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.

Approximate solutions of dual fuzzy polynomials by feed-back neural networks

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Ahmad Jafarian

2012-11-01

Full Text Available Recently, artificial neural networks (ANNs have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form $a_{1}x+ ...+a_{n}x^n =b_{1}x+ ...+b_{n}x^n+d,$ where $a_{j},b_{j},d epsilon E^1 (for j=1,...,n.$ Since the operation of fuzzy neural networks is based on Zadeh's extension principle. For this scope we train a fuzzified neural network by back-propagation-type learning algorithm which has five layer where connection weights are crisp numbers. This neural network can get a crisp input signal and then calculates its corresponding fuzzy output. Presented method can give a real approximate solution for given polynomial by using a cost function which is defined for the level sets of fuzzy output and target output. The simulation results are presented to demonstrate the efficiency and effectiveness of the proposed approach.

When compliments don't hit but critiques do: an fMRI study into self-esteem and self-knowledge in processing social feedback.

Science.gov (United States)

van Schie, C C; Chiu, C D; Rombouts, S A R B; Heiser, W J; Elzinga, B M

2018-02-27

The way we view ourselves may play an important role in our responses to interpersonal interactions. In this study, we investigate how feedback valence, consistency of feedback with self-knowledge and global self-esteem influence affective and neural responses to social feedback. Participants (N = 46) with a high range of self-esteem levels performed the social feedback task in an MRI scanner. Negative, intermediate and positive feedback was provided, supposedly by another person based on a personal interview. Participants rated their mood and applicability of feedback to the self. Analyses on trial basis on neural and affective responses are used to incorporate applicability of individual feedback words. Lower self-esteem related to low mood especially after receiving non-applicable negative feedback. Higher self-esteem related to increased PCC and precuneus activation (i.e., self-referential processing) for applicable negative feedback. Lower self-esteem related to decreased mPFC, insula, ACC and PCC activation (i.e, self-referential processing) during positive feedback and decreased TPJ activation (i.e., other referential processing) for applicable positive feedback. Self-esteem and consistency of feedback with self-knowledge appear to guide our affective and neural responses to social feedback. This may be highly relevant for the interpersonal problems that individuals face with low self-esteem and negative self-views.

Sensitivity of system stability to model structure

Science.gov (United States)

Hosack, G.R.; Li, H.W.; Rossignol, P.A.

2009-01-01

A community is stable, and resilient, if the levels of all community variables can return to the original steady state following a perturbation. The stability properties of a community depend on its structure, which is the network of direct effects (interactions) among the variables within the community. These direct effects form feedback cycles (loops) that determine community stability. Although feedback cycles have an intuitive interpretation, identifying how they form the feedback properties of a particular community can be intractable. Furthermore, determining the role that any specific direct effect plays in the stability of a system is even more daunting. Such information, however, would identify important direct effects for targeted experimental and management manipulation even in complex communities for which quantitative information is lacking. We therefore provide a method that determines the sensitivity of community stability to model structure, and identifies the relative role of particular direct effects, indirect effects, and feedback cycles in determining stability. Structural sensitivities summarize the degree to which each direct effect contributes to stabilizing feedback or destabilizing feedback or both. Structural sensitivities prove useful in identifying ecologically important feedback cycles within the community structure and for detecting direct effects that have strong, or weak, influences on community stability. The approach may guide the development of management intervention and research design. We demonstrate its value with two theoretical models and two empirical examples of different levels of complexity. ?? 2009 Elsevier B.V. All rights reserved.

Stabilization of model-based networked control systems

Energy Technology Data Exchange (ETDEWEB)

Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)

2016-06-08

A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

Bayesian feedback versus Markovian feedback in a two-level atom

International Nuclear Information System (INIS)

Wiseman, H.M.; Mancini, Stefano; Wang Jin

2002-01-01

We compare two different approaches to the control of the dynamics of a continuously monitored open quantum system. The first is Markovian feedback, as introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. 70, 548 (1993)]. The second is feedback based on an estimate of the system state, developed recently by Doherty and Jacobs [Phys. Rev. A 60, 2700 (1999)]. Here we choose to call it, for brevity, Bayesian feedback. For systems with nonlinear dynamics, we expect these two methods of feedback control to give markedly different results. The simplest possible nonlinear system is a driven and damped two-level atom, so we choose this as our model system. The monitoring is taken to be homodyne detection of the atomic fluorescence, and the control is by modulating the driving. The aim of the feedback in both cases is to stabilize the internal state of the atom as close as possible to an arbitrarily chosen pure state, in the presence of inefficient detection and other forms of decoherence. Our results (obtained without recourse to stochastic simulations) prove that Bayesian feedback is never inferior, and is usually superior, to Markovian feedback. However, it would be far more difficult to implement than Markovian feedback and it loses its superiority when obvious simplifying approximations are made. It is thus not clear which form of feedback would be better in the face of inevitable experimental imperfections

Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.

Science.gov (United States)

Gibson, Alison; Artemiadis, Panagiotis

2014-01-01

As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.

Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Yan, Ji; Bao-Tong, Cui

2010-01-01

In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB. (general)

Generalized fast feedback system in the SLC

International Nuclear Information System (INIS)

Hendrickson, L.; Allison, S.; Gromme, T.; Himel, T.; Krauter, K.; Rouse, F.; Sass, R.; Shoaee, H.

1992-01-01

A generalized fast feedback system has been developed to stabilize beams at various locations in the SLC. The system is designed to perform measurements and change actuator settings to control beam states such as position, angle and energy on a pulse to pulse basis. The software design is based on the state space formalism of digital control theory. The system is database-driven, facilitating the addition of new loops without requiring additional software. A communications system, KISNet, provides fast communications links between microprocessors for feedback loops which involve multiple micros. Feedback loops have been installed in seventeen locations throughout the SLC and have proven to be invaluable in stabilizing the machine. (author)

Neurophysiological correlates of anhedonia in feedback processing

NARCIS (Netherlands)

G.W. Mies (Gabry); I. van den Berg (Ivo); I.H.A. Franken (Ingmar); M. Smits (Marion); M.W. Molen, van der (Maurits); F.M. van der Veen (Frederik)

2013-01-01

textabstractDisturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a key role have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study

Neurophysiological correlates of anhedonia in feedback processing

NARCIS (Netherlands)

Mies, G.W.; Berg, I. van den; Franken, I.H.A.; Smits, M.; Molen, M.W. van der; Veen, F.M. van der

2013-01-01

Disturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a keyrole have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study investigated the

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

Directory of Open Access Journals (Sweden)

Wang Chao

2016-03-01

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

Exponential Stabilization of an Underactuated Surface Vessel

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Kristin Y. Pettersen

1997-07-01

Full Text Available The paper shows that a large class of underactuated vehicles cannot be asymptotically stabilized by either continuous or discontinuous state feedback. Furthermore, stabilization of an underactuated surface vessel is considered. Controllability properties of the surface vessels is presented, and a continuous periodic time-varying feedback law is proposed. It is shown that this feedback law exponentially stabilizes the surface vessel to the origin, and this is illustrated by simulations.

Studies of stability and robustness for artificial neural networks and boosted decision trees

International Nuclear Information System (INIS)

Yang, H.-J.; Roe, Byron P.; Zhu Ji

2007-01-01

In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to classify events given a number of identification variables. The BDT algorithm has been discussed by us in previous publications. Testing is done in this paper by smearing and shifting the input variables of testing samples. Based on these studies, BDT has better particle identification performance than ANN. The degradation of the classifications obtained by shifting or smearing variables of testing results is smaller for BDT than for ANN

Global stability and existence of periodic solutions of discrete delayed cellular neural networks

International Nuclear Information System (INIS)

Li Yongkun

2004-01-01

We use the continuation theorem of coincidence degree theory and Lyapunov functions to study the existence and stability of periodic solutions for the discrete cellular neural networks (CNNs) with delays xi(n+1)=xi(n)e-bi(n)h+θi(h)-bar j=1maij(n)fj(xj(n))+θi(h)-bar j=1mbij(n)fj(xj(n- τij(n)))+θi(h)Ii(n),i=1,2,...,m. We obtain some sufficient conditions to ensure that for the networks there exists a unique periodic solution, and all its solutions converge to such a periodic solution

[On the principle of substance stability and thermodynamic feedback in hierarchic systems of the bio-world].

Science.gov (United States)

Gladyshev, G P

2002-01-01

The creation of structural hierarchies in open natural biosystems within the framework of quasi-closed systems is investigated by the methods of hierarchic thermodynamics (thermostatics). During the evolution of natural open systems, every higher hierarchic level j appears as a consequence of thermodynamic self-organization (self-assembly) of the structures of the lower (j-1)-th level. Such a self-assembly proceeds as a result of stabilization of the j-th level. This is related to the Gibbs' (Helmholtz') specific function of formation of the structure of the j-th level tending to a minimum. As a result of action of the principle of substance (matter) stability, the structures of the j-th level are enriched with less stable structures of the (j-1)-th level in the course of evolution. This provides a thermodynamic feedback between the structures of the higher j-th level and lower (j-1)-th level, thus preventing full structural stabilization of the j-th level and causing "thermodynamic rejuvenation" of biosystems. The latter enhances "thermodynamic" deceleration of evolution and practically unlimited maintenance of life. Examples of quantitative correlations are provided that call for further application of the substance stability principle to living and nonliving hierarchic structures.

Top-down modulation on perceptual decision with balanced inhibition through feedforward and feedback inhibitory neurons.

Directory of Open Access Journals (Sweden)

Cheng-Te Wang

Full Text Available Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Top-down modulation on perceptual decision with balanced inhibition through feedforward and feedback inhibitory neurons.

Science.gov (United States)

Wang, Cheng-Te; Lee, Chung-Ting; Wang, Xiao-Jing; Lo, Chung-Chuan

2013-01-01

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

The neural and behavioral correlates of social evaluation in childhood

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Michelle Achterberg

2017-04-01

Full Text Available Being accepted or rejected by peers is highly salient for developing social relations in childhood. We investigated the behavioral and neural correlates of social feedback and subsequent aggression in 7–10-year-old children, using the Social Network Aggression Task (SNAT. Participants viewed pictures of peers that gave positive, neutral or negative feedback to the participant’s profile. Next, participants could blast a loud noise towards the peer, as an index of aggression. We included three groups (N = 19, N = 28 and N = 27 and combined the results meta-analytically. Negative social feedback resulted in the most behavioral aggression, with large combined effect-sizes. Whole brain condition effects for each separate sample failed to show robust effects, possibly due to the small samples. Exploratory analyses over the combined test and replication samples confirmed heightened activation in the medial prefrontal cortex (mPFC after negative social feedback. Moreover, meta-analyses of activity in predefined regions of interest showed that negative social feedback resulted in more neural activation in the amygdala, anterior insula and the mPFC/anterior cingulate cortex. Together, the results show that social motivation is already highly salient in middle childhood, and indicate that the SNAT is a valid paradigm for assessing the neural and behavioral correlates of social evaluation in children.

HTS flywheel energy storage system with rotor shaft stabilized by feed-back control of armature currents of motor-generator

International Nuclear Information System (INIS)

Tsukamoto, O.; Utsunomiya, A.