Novel global robust exponential stability criterion for uncertain BAM neural networks with time-varying delays

被引：18
|
作者：
Sheng, Li [1 ]
Yang, Huizhong [1 ]
机构：
[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Jiangsu, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2009年 / 40卷 / 05期
基金：
中国国家自然科学基金;
关键词：
ASSOCIATIVE MEMORY NETWORKS; LMI-BASED CRITERIA; ASYMPTOTIC STABILITY; DISCRETE; SYSTEMS; IMPULSES;
D O I：
10.1016/j.chaos.2007.09.098
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, the global robust exponential stability for a class of delayed BAM neural networks with norm-bounded uncertainty is studied. Some less conservative conditions are presented for the global exponential stability of BAM neural networks with time-varying delays by constructing a new class of Lyapunov functionals combined with free-weighting matrices, This novel approach, based oil the linear matrix inequality (LMI) technique, removes some existing restrictions on the system's parameters, and the derived conditions are easy to verify via the LMI toolbox. Comparisons between our results and previous results admit that our results establish a new set of stability criteria for delayed BAM neural networks. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2102 / 2113
页数：12
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