Mean square exponential stability of stochastic fuzzy delayed Cohen-Grossberg neural networks with expectations in the coefficients

被引：35
|
作者：
Xie, Wenjuan
Zhu, Quanxin [1 ]
机构：
[1] Nanjing Normal Univ, Sch Math Sci, Nanjing 210023, Jiangsu, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 166卷
基金：
中国国家自然科学基金;
关键词：
Stochastic fuzzy Cohen-Grossberg neural network; Time-varying delay; Expectation; Mean square exponential stability; Lyapunov-Krasovskii functional; TIME-VARYING DELAYS; GLOBAL STABILITY; CRITERIA;
D O I：
10.1016/j.neucom.2015.04.020
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, we consider a class of stochastic fuzzy Cohen-Grossberg neural networks with timevarying delays. Different from the previous literature, we introduce expectations into coefficients of the model. Under some reasonable conditions, we show that the trivial solution of the suggested system is exponentially stable in the mean square. Finally, an example is given to illustrate the effectiveness of the theoretical results. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：133 / 139
页数：7
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