An LMI approach to asymptotical stability of multi-delayed neural networks

被引：74

作者：

Liao, XF ^{[1
]}

Li, CD ^{[1
]}

机构：

[1] Chongqing Univ, Dept Comp Sci & Engn, Chongqing 400044, Peoples R China

来源：

PHYSICA D-NONLINEAR PHENOMENA | 2005年 / 200卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

neural networks; global asymptotic stability; Lyapunov-Krasovskii functional; linear matrix inequality; multiple time delays;

D O I：

10.1016/j.physd.2004.10.009

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, some criteria are derived for global asymptotic stability of a class of neural networks with multiple constant or time-varying delays. Based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach, some delay-independent criteria for neural networks with multiple constant delays and delay-dependent criteria for neural networks with multiple time-varying delays are provided to guarantee global asymptotic stability of these networks. The main results are generalizations of some recent results reported in the literature. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：139 / 155

页数：17

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