Novel delay-dependent stability criteria of neural networks with time-varying delay

被引：35

作者：

Chen, Yonggang ^{[1
]}

Wu, Yuanyuan ^{[2
]}

机构：

[1] Henan Inst Sci & Technol, Dept Math, Xinxiang 453003, Peoples R China

[2] Southeast Univ, Res Inst Automat, Nanjing 210096, Peoples R China

来源：

NEUROCOMPUTING | 2009年 / 72卷 / 4-6期

关键词：

Stability; Neural networks; Time-varying delay; Linear matrix inequalities (LMIs); GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; EXPONENTIAL STABILITY; UNCERTAINTIES;

D O I：

10.1016/j.neucom.2008.03.006

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the delay-dependent stability is investigated for neural networks with a time-varying delay. By using the augmented Lyapunov functional method and by resorting to the novel method for estimating the upper bound of the derivative of augmented Lyapunov functionals, the less conservative asymptotic stability criteria are derived in terms of linear matrix inequalities (LMIs). Two numerical examples are presented to show the effectiveness and the less conservativeness of the proposed method. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：1065 / 1070

页数：6

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