A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay

被引：49

作者：

Balasubramaniam, P. ^{[1
]}

Nagamani, G. ^{[1
]}

机构：

[1] Gandhigram Rural Univ, Dept Math, Gandhigram 624302, Tamil Nadu, India

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 10期

关键词：

Linear matrix inequality (LMI); Neural networks; Passivity; Time-varying delays; Lyapunov method; EXPONENTIAL STABILITY; SYSTEMS;

D O I：

10.1016/j.neucom.2011.01.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with delay-dependent passivity analysis for interval neural networks with time-varying delay. By decomposing the delay interval into multiple equidistant subintervals, new Lyapunov-Krasovskii functionals (LKFs) are constructed on these intervals. Employing these new LKFs, a new passivity criterion is proposed in terms of linear matrix inequalities, which is dependent on the size of the time delay. Finally, some numerical examples are given to illustrate the effectiveness of the developed techniques. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1646 / 1653

页数：8

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