Robust stability of discrete-time LPD neural networks with time-varying delay

被引：8

作者：

Udpin, S. ^{[1
]}

Niamsup, P. ^{[1
]}

机构：

[1] Chiang Mai Univ, Dept Math, Chiang Mai 50200, Thailand

来源：

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2009年 / 14卷 / 11期

关键词：

Neural network; Robust stability; Polytopic type uncertainties; Linear matrix inequality (LMI); Linear parameter dependent (LPD); Lyapunov function; S-procedure; SYSTEMS; UNCERTAINTIES;

D O I：

10.1016/j.cnsns.2008.08.018

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper presents a new approach to the robust stability of discrete-time LPD neural networks with time-varying delay and with normed bounded uncertainties as well as polytopic type uncertainties. Based on Lyapunov stability theory and the S-procedure, we derive robust stability criteria in terms of linear matrix inequalities (LMI) which are solvable by several available algorithms. We show that some of the existing results on robust stability of neural networks are corollaries of main results of this paper. Numerical examples are given to illustrate the effectiveness of our theoretical results. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：3914 / 3924

页数：11

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