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This paper deals with the exponential synchronization of coupled stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal de...
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This paper deals with the exponential synchronization of coupled stochastic memristor-based neural networks with probabilistic time-varying delay coupling and time-varying impulsive delay. There is one probabilistic transmittal delay in the delayed coupling that is translated by a Bernoulli stochastic variable satisfying a conditional probability distribution. The disturbance is described by a Wiener process. Based on Lyapunov functions, Halanay inequality, and linear matrix inequalities, sufficient conditions that depend on the probability distribution of the delay coupling and the impulsive delay were obtained. Numerical simulations are used to show the effectiveness of the theoretical results.
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By constructing proper vector Lyapunov functions and nonlinear integro-differential inequalities involving both variable delays and unbounded delays, and using M-matrix theory, several sufficient conditions are obtained. These con...
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By constructing proper vector Lyapunov functions and nonlinear integro-differential inequalities involving both variable delays and unbounded delays, and using M-matrix theory, several sufficient conditions are obtained. These conditions ensure the global exponential robust periodicity and stability of interval neural networks with both variable and unbounded delays. The assumptions on the boundedness of the activation functions and the differentiability of time-varying delays, needed in most other papers, are no longer necessary in the present study. The obtained results in this paper improve and extend those given in the earlier literature. (C) 2006 Elsevier Ltd. All rights reserved.
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In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for neural networks with parametric uncertainties and time delay are studied. Based on Lyapunov-Krasovskii...
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In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for neural networks with parametric uncertainties and time delay are studied. Based on Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee global robust exponential stability. The exponential convergence rate can be easily estimated via these criteria. (c) 2006 Published by Elsevier Ltd.
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In this paper cellular neural networks with mixed delays are considered. Sufficient conditions for the existence and exponential stability of the almost periodic solutions are established by using fixed point theorem, Lyapunov fun...
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In this paper cellular neural networks with mixed delays are considered. Sufficient conditions for the existence and exponential stability of the almost periodic solutions are established by using fixed point theorem, Lyapunov functional method and differential inequality technique. The results of this paper are new and they complement previously known results. (c) 2005 Elsevier Ltd. All rights reserved.
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This paper is concerned with stabilization of (time-varying) linear systems with a single time-varying input delay by using the predictor based delay compensation approach. Differently from the traditional predictor feedback which...
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This paper is concerned with stabilization of (time-varying) linear systems with a single time-varying input delay by using the predictor based delay compensation approach. Differently from the traditional predictor feedback which uses the open-loop system dynamics to predict the future state and will result in an infinite dimensional controller, we propose in this paper a pseudo-predictor feedback (PPF) approach which uses the (artificial) closed-loop system dynamics to predict the future state and the resulting controller is finite dimensional and is thus easy to implement. Necessary and sufficient conditions guaranteeing the stability of the closed-loop system under the PPF are obtained in terms of the stability of a class of integral delay operators (systems). Moreover, it is shown that the PPF can compensate arbitrarily large yet bounded input delays provided the open-loop (time-varying linear) system is only polynomially unstable and the feedback gain is well designed. Comparison of the proposed PPF approach with the existing results is well explored. Numerical examples demonstrate the effectiveness of the proposed approaches. (C) 2014 Elsevier Ltd. All rights reserved.
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Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential...
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Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.
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In this paper, the robust exponential synchronization problem for a class of neutral complex networks with discrete and distributed time-varying delays is investigated. Some delay-dependent synchronization criteria are derived by ...
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In this paper, the robust exponential synchronization problem for a class of neutral complex networks with discrete and distributed time-varying delays is investigated. Some delay-dependent synchronization criteria are derived by using the descriptor model transformation method; the stability condition of error dynamical networks based on the Lyapunov-Krasovskii functional is obtained via linear matrix inequality (LMI) formulation. Finally, numerical examples are presented to show the effectiveness of the proposed theoretical results.
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A new method of explicitly adaptive time delay estimation (EATDE) algorithm is proposed for estimating a varying time delay parameter. The proposed method is based on the Haar wavelet transform of cross-correlations. The proposed ...
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A new method of explicitly adaptive time delay estimation (EATDE) algorithm is proposed for estimating a varying time delay parameter. The proposed method is based on the Haar wavelet transform of cross-correlations. The proposed algorithm can be viewed as a gradientbased optimization of lowpass filtered cross-correlations, but requires less computational power. The algorithm shows a global convergence property for wide-band signals with uncorrelated noises. A convergence analysis including mean behavior, mean-square-error behavior, and steady-state error of delay estimate is given. Simulation results are also provided to demonstrate the performance of the proposed algorithm.
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This paper is concerned with pth moment exponential stability of stochastic reaction-diffusionCohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method,stochastic analysis, and inequality technique...
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This paper is concerned with pth moment exponential stability of stochastic reaction-diffusionCohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method,stochastic analysis, and inequality techniques, a set of new suffcient conditions on pth momentexponential stability for the considered system is presented. The proposed results generalized andimproved some earlier publications.
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In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which ...
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In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several performance measures, such as the performance, passivity, – performance, and dissipativity. By introducing a triple-summable term in Lyapunov function, the reciprocally convex approach is utilized to bound the forward difference of the triple-summable term and then the extended dissipativity criterion for discrete-time neural networks with time-varying delay is established. The derived condition guarantees not only the extended dissipativity but also the stability of the neural networks. Two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results.
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