pth moment exponential stability of stochastic recurrent neural networks with time-varying delays

被引：95
|
作者：
Sun, Yonghui [1 ]
Cao, Jinde [1 ]
机构：
[1] SE Univ, Dept Math, Nanjing 210096, Peoples R China
来源：
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2007年 / 8卷 / 04期
基金：
中国国家自然科学基金;
关键词：
stochastic recurrent neural networks; pth moment exponential stability; time-varying delays; Burkholder-Davis-Gundy inequality;
D O I：
10.1016/j.nonrwa.2006.06.009
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, the issue of pth moment exponential stability of stochastic recurrent neural network with time-varying delays is investigated in detail. Employing the method of variation parameter and inequality techniques, several sufficient conditions ensuring pth moment exponential stability are obtained. Compared with the previous methods, our method does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier works reported in the literature. Two numerical examples are given to illustrate the effectiveness of our results. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1171 / 1185
页数：15
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