LMI Approach for Stability in Stochastic Delayed Neural Systems

被引：0
|
作者：
Lou, Xuyang [1 ]
Qiao, Yong [1 ]
Cui, B. T. [1 ]
Ye, Q. [2 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Wuxi Inst Technol, Coll Internet Things Technol, Wuxi 214121, Peoples R China
来源：
2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) | 2014年
基金：
中国国家自然科学基金;
关键词：
Neural networks; Halanay inequality; Lyapunov functional; Linear matrix inequality; EXPONENTIAL STABILITY; TIME DELAYS; NETWORKS;
D O I：
暂无
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 asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with time-varying delays. We aim to construct easily verifiable conditions for the asymptotic stability in the mean square of the delayed neural networks. Via a Lyapunov functional and the Halanay inequality technique, several stability criteria are derived. Two examples are provided to illustrate the effectiveness and applicability of the proposed criteria.
引用
收藏
页码：52 / 57
页数：6
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