Mean-square exponential stability for stochastic discrete-time recurrent neural networks with mixed time delays

被引：48
|
作者：
Li, Jian-Ning [1 ]
Li, Lin-Sheng [2 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Automat, Hangzhou 310018, Zhejiang, Peoples R China
[2] Taiyuan Univ Sci & Technol, Taiyuan 030024, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 151卷
基金：
中国国家自然科学基金;
关键词：
Discrete-time recurrent neural networks; Mixed time-delays; Mean-square exponential stability; Stochastic system; Linear matrix inequalities (LMIs); DEPENDENT STABILITY; CRITERIA; SYSTEMS;
D O I：
10.1016/j.neucom.2014.10.020
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the mean-square exponential stability problem for discrete-time recurrent neural networks with time-varying discrete and distributed delays is investigated. Considering the delay distributions, a novel class of Lyapunov functional is introduced. By exploiting all possible information in mixed time delays, a sufficient condition for the whole system to be mean-square exponentially stable is given. Numerical examples are proposed to illustrate the effectiveness of the method, and show that by using the approach in this paper, the obtained results are less conservative than the existing ones. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：790 / 797
页数：8
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