Mean-square exponentially input-to-state stability of stochastic Cohen-Grossberg neural networks with time-varying delays

被引：37
作者：
Zhou, Weisong [1 ]
Teng, Lingying [2 ]
Xu, Daoyi [1 ]
机构：
[1] Sichuan Univ, Yangtze Ctr Math, Chengdu 610064, Sichuan, Peoples R China
[2] Southwest Univ Nationalities, Coll Comp Sci & Technol, Chengdu 610041, Sichuan, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 153卷
基金：
中国国家自然科学基金;
关键词：
Mean-square exponentially input-to-state stable; Cohen-Grossberg neural networks; Razumikhin technique; Time-varying delays; GLOBAL ATTRACTING SET; PERIODIC-SOLUTION; STABILIZATION; BOUNDEDNESS; EXISTENCE; SYSTEMS;
D O I：
10.1016/j.neucom.2014.11.052
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, a class of stochastic Cohen-Grossberg neural networks with time-varying delays is considered. By utilizing Razumikhin technique and constructing new Halanay differential inequalities, some sufficient conditions ensuring the mean-square exponential input-to-state stability of the networks are obtained. Two numerical examples are given to illustrate the efficiency of the derived results. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：54 / 61
页数：8
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