On Stabilization of Stochastic Cohen-Grossberg Neural Networks With Mode-Dependent Mixed Time-Delays and Markovian Switching

被引：64

作者：

Zheng, Cheng-De ^{[1
]}

Shan, Qi-He ^{[2
]}

Zhang, Huaguang ^{[2
]}

Wang, Zhanshan ^{[2
]}

机构：

[1] Dalian Jiaotong Univ, Dept Math, Dalian 116028, Peoples R China

[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2013年 / 24卷 / 05期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Exponential stabilization; Markovian jumping parameters; mixed mode-dependent time-delays; stochastic Cohen-Grossberg neural networks; GLOBAL ASYMPTOTIC STABILITY; EXPONENTIAL STABILITY; VARYING DELAYS; ACTIVATION FUNCTIONS; DISCRETE; INTERVAL; SYSTEMS; CRITERIA;

D O I：

10.1109/TNNLS.2013.2244613

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. By introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive delay-dependent criteria for the exponential stabilization problem. Three numerical examples are carried out to demonstrate the feasibility of our delay-dependent stabilization criteria.

引用

页码：800 / 811

页数：12

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