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
收藏
页码：1742 / 1748
页数：7
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