ROBUST EXPONENTIAL STABILITY OF STOCHASTIC REACTION-DIFFUSION RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS AND MODE-DEPENDENT DELAYS

被引：0
|
作者：
Kao, Y. [1 ]
Zhang, P.
Shi, L.
Sun, X.
机构：
[1] Harbin Inst Technol, Dept Math Sci, Weihai 264209, Peoples R China
来源：
DYNAMIC SYSTEMS AND APPLICATIONS | 2014年 / 23卷 / 2-3期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
TIME-VARYING DELAYS; GLOBAL ASYMPTOTIC STABILITY; ALMOST-PERIODIC SOLUTIONS; DISTRIBUTED DELAYS; LINEAR-SYSTEMS; ACTIVATION FUNCTIONS; DYNAMICS ANALYSIS; LMI APPROACH; SYNCHRONIZATION; CRITERIA;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper is devoted to investigating global robust exponential stability for a class of delayed stochastic reaction-diffusion recurrent neural networks. The network parameters are governed by a continuous-time discrete-state Markov process which takes values in a finite set. By employing a Lyapunov-Krasovskii functional and some inequalities, some easy-to-test criteria on global exponential stability for this kind of stochastic neural networks are established in the form of linear matrix inequalities. An example is presented to illustrate the effectiveness of the theoretical results.
引用
收藏
页码：391 / 414
页数：24
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