78 research outputs found
Stability analysis of impulsive stochastic CohenāGrossberg neural networks with mixed time delays
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic CohenāGrossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic CohenāGrossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany
New Stability Criterion for Takagi-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Probabilistic Time-Varying Delays
A new global asymptotic stability criterion of Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with probabilistic time-varying delays was derived, in which the diffusion item can play its role. Owing to deleting the boundedness conditions on amplification functions, the main result is a novelty to some extent. Besides, there is another novelty in methods, for Lyapunov-Krasovskii functional is the positive definite form of p powers, which is different from those of existing literature. Moreover, a numerical example illustrates the effectiveness of the proposed methods
Synchronization of Chaotic Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Sampled-Data Control
This paper investigates the synchronization problem for neural networks with leakage delay and both discrete and distributed time-varying delays under sampled-data control. By employing the Lyapunov functional method and using the matrix inequality techniques, a delay-dependent LMIs criterion is given to ensure that the master systems and the slave systems are synchronous. An example with simulations is given to show the effectiveness of the proposed criterion
Anti-periodic solution for fuzzy CohenāGrossberg neural networks with time-varying and distributed delays
In this paper, by using a continuation theorem of coincidence degree theory and a differential inequality, we establish some sufficient conditions ensuring the existence and global exponential stability of anti-periodic solutions for a class of fuzzy CohenāGrossberg neural networks with time-varying and distributed delays. In addition, we present an illustrative example to show the feasibility of obtained results
Mean Square Exponential Stability of Stochastic Cohen-Grossberg Neural Networks with Unbounded Distributed Delays
This paper addresses the issue of mean square
exponential stability of stochastic Cohen-Grossberg neural
networks (SCGNN), whose state variables are described by
stochastic nonlinear integrodifferential equations. With the
help of Lyapunov function, stochastic analysis technique, and
inequality techniques, some novel sufficient conditions on mean
square exponential stability for SCGNN are given. Furthermore,
we also establish some sufficient conditions for checking
exponential stability for Cohen-Grossberg neural networks with
unbounded distributed delays
Stability results for impulsive functional differential equations with infinite delay
For a family of diff erential equations with in finitive delay and impulses, we establish
conditions for the existence of global solutions and for the global asymptotic and global
exponential stabilities of an equilibrium point. The results are used to give stability
criteria for a very broad family of impulsive neural network models with both unbounded
distributed delays and bounded time-varying discrete delays. Most of the impulsive
neural network models with delay recently studied are included in the general framework
presented here.FundaĆ§Ć£o para a CiĆŖncia e a Tecnologia (FCT
Novel Lagrange sense exponential stability criteria for time-delayed stochastic CohenāGrossberg neural networks with Markovian jump parameters: A graph-theoretic approach
This paper concerns the issues of exponential stability in Lagrange sense for a class of stochastic CohenāGrossberg neural networks (SCGNNs) with Markovian jump and mixed time delay effects. A systematic approach of constructing a global Lyapunov function for SCGNNs with mixed time delays and Markovian jumping is provided by applying the association of Lyapunov method and graph theory results. Moreover, by using some inequality techniques in Lyapunov-type and coefficient-type theorems we attain two kinds of sufficient conditions to ensure the global exponential stability (GES) through Lagrange sense for the addressed SCGNNs. Ultimately, some examples with numerical simulations are given to demonstrate the effectiveness of the acquired result
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