Delay-dependent robust stability analysis for Markovian jumping stochastic Cohen-Grossberg neural networks with discrete interval and distributed time-varying delays

被引：45

作者：

Balasubramaniam, P. ^{[1
]}

Rakkiyappan, R. ^{[1
]}

机构：

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

来源：

NONLINEAR ANALYSIS-HYBRID SYSTEMS | 2009年 / 3卷 / 03期

关键词：

Delay/interval-dependent stability; Linear matrix inequality; Lyapunov-Krasovskii functional; Markovian jumping parameters; Stochastic neural networks; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; STATE ESTIMATION; CRITERIA;

D O I：

10.1016/j.nahs.2009.01.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with discrete interval and distributed delays. The parameter uncertainties are assumed to be norm bounded and the discrete delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free weight matrix via Newton-Leibniz formula is required. Two numerical examples are provided to show that the proposed results significantly improve the allowable upper and lower bounds of delays over some existing results in the literature. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：207 / 214

页数：8

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