Global robust stability of delayed recurrent neural networks

被引：240
|
作者：
Cao, JD [1 ]
Huang, DS
Qu, YZ
机构：
[1] SE Univ, Dept Math, Nanjing 210096, Peoples R China
[2] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
[3] SE Univ, Dept Comp Sci & Engn, Nanjing 210096, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2005年 / 23卷 / 01期
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1016/j.chaos.2004.04.002
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper is concerned with the global robust stability of a class of delayed interval recurrent neural networks which contain time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. A new sufficient condition is presented for the existence, uniqueness, and global robust stability of equilibria for interval neural networks with time delays by constructing Lyapunov functional and using matrix-norm inequality. An error is corrected in an earlier publication, and an example is given to show the effectiveness of the obtained results. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：221 / 229
页数：9
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