Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control

被引：84
|
作者：
Li, Xiaofan [1 ,2 ]
Fang, Jian-an [2 ]
Li, Huiyuan [1 ]
机构：
[1] Yancheng Inst Technol, Sch Elect Engn, Yancheng 224051, Peoples R China
[2] Donghua Univ, Sch Informat Sci & Technol, Shanghai 201620, Peoples R China
来源：
NEURAL NETWORKS | 2017年 / 93卷
关键词：
Complex-value memristor-based neural networks; Exponential synchronization; Time-varying delays; Impulsive control; STOCHASTIC DYNAMICAL NETWORKS; TIME-VARYING DELAYS; STABILITY; DISCRETE;
D O I：
10.1016/j.neunet.2017.05.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：165 / 175
页数：11
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