Stability analysis of discrete-time stochastic neural networks with time-varying delays

被引：49

作者：

Ou, Yan ^{[1
]}

Liu, Hongyang ^{[1
]}

Si, Yulin ^{[1
]}

Feng, Zhiguang ^{[2
]}

机构：

[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150001, Heilongjiang, Peoples R China

[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China

来源：

NEUROCOMPUTING | 2010年 / 73卷 / 4-6期

关键词：

Asymptotic stability; Delay partitioning; Discrete-time neural networks; Stochastic neural networks; Linear matrix inequality; Time-varying delays; ROBUST EXPONENTIAL STABILITY; SYSTEMS; STABILIZATION; CRITERIA;

D O I：

10.1016/j.neucom.2009.10.017

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 investigates the problem of stability analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays. In the concerned model, stochastic disturbances are described by a Brownian motion, and time-varying delay d(k) satisfies d(m) <= d(k) <= d(M). Based on the delay partitioning idea and some inequalities, a new stability criterion with less conservatism in terms of linear matrix inequalities (LMIs) is proposed by introducing a novel Lyapunov-Krasovskii functional combined with a free-weighting matrix method. The condition can be checked by utilizing some numerical software and a numerical example is provided to show the usefulness of the proposed condition. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：740 / 748

页数：9

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