Robust stability analysis of uncertain stochastic neural networks with interval time-varying delay

被引：31
|
作者：
Feng, Wei [1 ,3 ]
Yang, Simon X. [1 ,2 ]
Fu, Wei [1 ]
Wu, Haixia [3 ]
机构：
[1] Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China
[2] Univ Guelph, Sch Engn, Guelph, ON N1G 2W1, Canada
[3] Chongqing Educ Coll, Dept Comp & Modern Educ Technol, Chongqing 400067, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2009年 / 41卷 / 01期
关键词：
H-INFINITY CONTROL; EXPONENTIAL STABILITY; DEPENDENT STABILITY; LINEAR-SYSTEMS;
D O I：
10.1016/j.chaos.2008.01.024
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper addresses the stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. The parameter uncertainties are assumed to be norm bounded, and the delay factor 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. A sufficient condition is derived such that for all admissible uncertainties, the considered neural network is robustly, globally, asymptotically stable in the mean square. Some stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which call be effectively solved by some standard numerical packages. Finally, numerical examples are provided to demonstrate the usefulness of the proposed criteria. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：414 / 424
页数：11
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