Asymptotic stability analysis of stochastic reaction-diffusion Cohen-Grossberg neural networks with mixed time delays

被引：19

作者：

Shi, Yanchao ^{[1
]}

Zhu, Peiyong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math, Chengdu 611731, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 242卷

关键词：

Asymptotic stability; Cohen-Grossberg neural network; Stochastic system; Lyapunov-Krasovskii functional; Linear matrix inequality; DISTRIBUTED DELAYS; VARYING DELAYS; EXPONENTIAL STABILITY; CRITERIA; INEQUALITY; COMPLEXITY; TERMS;

D O I：

10.1016/j.amc.2014.05.056

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, the asymptotic stability problem is studied for a class of stochastic Cohen-Grossberg neural networks with reaction-diffusion and time-mixed delays. By using the Lyapunov-Krasovskii functional, stochastic analysis technology and linear matrix inequalities (LMIs) technique, several sufficient conditions on the asymptotic stability for the considered system are obtained. The condition not only connects with the delays and diffusion effect, but also relates to the magnitude of noise. Therefore, these stability criteria are essentially new and more effective than those given in previous conditions. Two examples are presented to illustrate the effectiveness and efficiency of the results. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：159 / 167

页数：9

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