Global robust exponential stability of delayed neural networks: An LMI approach

被引：12

作者：

Ou, Ou ^{[1
]}

机构：

[1] Chengdu Univ Technol, Coll Informat Engn, Chengdu 610059, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2007年 / 32卷 / 05期

关键词：

D O I：

10.1016/j.chaos.2005.12.026

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for neural networks with parametric uncertainties and time delay are studied. Based on Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee global robust exponential stability. The exponential convergence rate can be easily estimated via these criteria. (c) 2006 Published by Elsevier Ltd.

引用

页码：1742 / 1748

页数：7

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