Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays

被引：75
作者：
Bao, Haibo [1 ,2 ]
Park, Ju H. [2 ]
Cao, Jinde [3 ,4 ]
机构：
[1] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China
[2] Yeungnam Univ, Dept Elect Engn, Nonlinear Dynam Grp, Kyongsan 38541, South Korea
[3] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[4] King Abdulaziz Univ, Dept Math, Fac Sci, Jeddah 21589, Saudi Arabia
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2015年 / 270卷
基金：
中国国家自然科学基金;
关键词：
Exponential synchronization; Anti-synchronization; Matrix measure; Memristor-based neural networks; Linear control; Time-varying delays; COMPLEX DYNAMICAL NETWORK; GLOBAL SYNCHRONIZATION; CHAOTIC SYSTEMS; STABILITY; DISCRETE; ELEMENT;
D O I：
10.1016/j.amc.2015.08.064
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper is concerned with exponential synchronization and anti-synchronization of memristor-based neural networks. Under the framework of Filippov systems and a linear controller, the exponential synchronization and anti-synchronization criteria for memristor-based neural networks can be guaranteed by the matrix measure and Halanay inequality. The criteria are very simple to implement in practice. Finally, two numerical examples are given to demonstrate the correctness of the theoretical results. It is shown that the matrix measure can increase the exponential convergence rate and decrease the feedback gain effectively. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码：543 / 556
页数：14
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