ALMOST SURE EXPONENTIAL STABILITY OF STOCHASTIC RECURRENT NEURAL NETWORKS WITH TIME-VARYING DELAYS

被引：3

作者：

Wan, Li ^{[1
]}

Zhou, Qinghua ^{[2
]}

机构：

[1] Wuhan Univ Sci & Engn, Dept Math & Phys, Wuhan 430073, Peoples R China

[2] Zhaoqing Univ, Dept Math, Zhaoqing 526061, Peoples R China

来源：

INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS | 2010年 / 20卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Stochastic recurrent neural networks; delays; almost sure exponential stability; GLOBAL ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; PERIODIC-SOLUTIONS; CONVERGENCE;

D O I：

10.1142/S0218127410025594

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The stability of stochastic recurrent neural networks with time-varying delays is investigated. A set of novel sufficient conditions on almost sure exponential stability has been established. Two examples are also given to illustrate the effectiveness of our results.

引用

页码：539 / 544

页数：6

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