A mode-dependent stability criterion for delayed discrete-time stochastic neural networks with Markovian jumping parameters

被引：21

作者：

Ou, Yan ^{[1
]}

Shi, Peng ^{[2
]}

Liu, Hongyang ^{[1
]}

机构：

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

[2] Univ Glamorgan, Fac Adv Technol, Pontypridd CF37 1DL, M Glam, Wales

来源：

NEUROCOMPUTING | 2010年 / 73卷 / 7-9期

关键词：

Free-weighting matrix method; Markovian jumping parameters; Mode-dependent delay; Stochastic neural networks; ROBUST EXPONENTIAL STABILITY; VARYING DELAYS; COMPLEX NETWORKS; SYSTEMS; SYNCHRONIZATION; UNCERTAINTY; DESIGN;

D O I：

10.1016/j.neucom.2009.11.004

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 for a class of discrete-time stochastic neural networks (DSNNs) with mode-dependent delay and Markovian jumping parameters. Throughout this paper, we assume that stochastic disturbances are described by the Brownian motion, jumping parameters are generated from discrete-time discrete-state homogeneous Markov process, and mode-dependent delay d(r(k)) satisfies d(m) <= d(r(k)) d(M). By a novel Lyapunov-Krasovskii functional combining with the delay partitioning technique and the free-weighting matrix method in terms of linear matrix inequalities (LMIs), the new stability criterion proves to be less conservative. Finally, numerical examples are given to illustrate the effectiveness of the proposed method. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：1491 / 1500

页数：10

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