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

被引：18

作者：

Xiong, WeiLi ^{[1
]}

Xu, BaoGuo ^{[1
]}

机构：

[1] So Yangtze Univ, Res Ctr Control Sci & Engn, Jiangsu 214122, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2008年 / 36卷 / 05期

关键词：

D O I：

10.1016/j.chaos.2006.09.065

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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. (C) 2006 Elsevier Ltd. All rights reserved.

引用

页码：1357 / 1365

页数：9

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