Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays

被引：50

作者：

Balasubramaniam, P. ^{[1
]}

Rakkiyappan, R. ^{[1
]}

机构：

[1] Gandhigram Rural Univ, Dept Math, Gandhigram 624302, Tamil Nadu, India

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 204卷 / 02期

关键词：

Global asymptotic stability; Linear matrix inequality; Lyapunov-Krasovskii functional; Multiple time-varying delays; Stochastic recurrent neural networks; Unbounded distributed delays;

D O I：

10.1016/j.amc.2008.05.001

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper using Lyapunov-Krasovskii functional and the linear matrix inequality ( LMI) approach the global asymptotic stability of stochastic recurrent neural networks with multiple discrete time-varying delays and distributed delays is analyzed. A new sufficient condition ensuring the global asymptotic stability for delayed recurrent neural networks is obtained in the stochastic sense using the powerful MATLAB LMI toolbox. Two examples are provided to illustrate the applicability of the stability results. (C) 2008 Elsevier Inc. All rights reserved.

引用

页码：680 / 686

页数：7

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