Pinning synchronization of memristor-based neural networks with time-varying delays

被引：49
|
作者：
Yang, Zhanyu [1 ]
Luo, Biao [2 ]
Liu, Derong [3 ]
Li, Yueheng [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[3] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
来源：
NEURAL NETWORKS | 2017年 / 93卷
基金：
中国国家自然科学基金;
关键词：
Memristor-based neural networks; Pinning control; Time-varying delays; Exponential synchronization; Asymptotic synchronization; EXPONENTIAL LAG SYNCHRONIZATION; IMPULSIVE SYNCHRONIZATION; ADAPTIVE SYNCHRONIZATION; CHAOTIC SYSTEMS; FUZZY MODEL; STABILIZATION; STABILITY; DESIGN;
D O I：
10.1016/j.neunet.2017.05.003
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 synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：143 / 151
页数：9
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