Global robust asymptotical stability of multi-delayed interval neural networks: an LMI approach

被引：78
|
作者：
Li, CD [1 ]
Liao, XF
Zhang, RN
机构：
[1] Chongqing Univ, Dept Comp Sci & Engn, Chongqing 400030, Peoples R China
[2] Chongqing Univ, Coll Business Adm, Chongqing 400030, Peoples R China
来源：
PHYSICS LETTERS A | 2004年 / 328卷 / 06期
基金：
中国国家自然科学基金;
关键词：
robust stability; interval delayed neural networks (IDNN); Lyapunov functional; linear matrix inequality (LMI); multiple time-varying delays;
D O I：
10.1016/j.physleta.2004.06.053
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria for interval neural networks (IDNN) with multiple time-varying delays are derived to guarantee global robust asymptotic stability. The main results are generalizations of some recent results reported in the literature. Numerical example is also given to show the effectiveness of our results. (C) 2004 Elsevier B.V. All rights reserved.
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页码：452 / 462
页数：11
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