Improved delay-dependent stability criteria for discrete-time stochastic neural networks with time-varying delays

被引：2

作者：

Luo, Meng-zhuo ^{[1
]}

Zhong, Shou-ming ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math & Sci, Chengdu 611731, Peoples R China

来源：

CEIS 2011 | 2011年 / 15卷

关键词：

Delay-dependent stability; Neural network; Time-varying delay; Lyapunov-Krasovskii; Linear matrix inequalities;

D O I：

10.1016/j.proeng.2011.08.837

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter, investigates the problem of mean square exponential stability for a class of discrete-time stochastic neural network with time-varying delays. By constructing a appropriate Lyapunov-Krasovskii functional, combining the stochastic stability theory, and the convex theory method, a delay-dependent exponential stability criteria is obtained in term of LMIs. Finally, a numerical example is exploited to show the usefulness of the results derived.

引用

页数：5

共 4 条

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Cichoki A., 1993, NEURAL NETWORKS OPTI

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APPLIED MATHEMATICS AND COMPUTATION, 2009, 209 (02) :305-313

[3]

Stuart A., 1998, Dynamical systems and numerical analysis

[4] Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays [J].

Tang, Yang ;

Fang, Jian-an ;

Xia, Min ;

Yu, Dongmei .

NEUROCOMPUTING, 2009, 72 (16-18) :3830-3838

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