New delay-dependent stability criterion for stochastic recurrent neural networks

被引：0

作者：

Yang, Liyun ^{[1
]}

Ren, Mifeng ^{[1
]}

Hao, Dongbo ^{[2
]}

Yang, Liqin ^{[3
]}

机构：

[1] Hebei Univ Sci & Technol, Coll Sci, Shijiazhuang 050018, Hebei, Peoples R China

[2] Hebei Poshing Elect Technol Co Ltd, Shijiazhuang 050200, Hebei, Peoples R China

[3] Chengezhuang Middle Sch, Qinhuangdao 066600, Hebei, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL | 2010年 / 11卷 / 3-4期

关键词：

stochastic recurrent neutral networks; time-varying delay; delay-dependent; robust stability;

D O I：

10.1504/IJMIC.2010.037026

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 problem of delay-dependent robust stability for uncertain stochastic neural networks with time-varying delay is considered. Based on Lyapunov stability theory combined with linear matrix inequalities (LMI) techniques, some new delay-dependent stability criteria in terms of LMI are derived by introducing some free weighing matrices and using Leibniz-Newton formula which can be selected properly to lead to less conservative results. Finally, two examples are given. One is given to illustrate, the other is an extended model for prevenient systems.

引用

页码：164 / 172

页数：9

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