Exponential synchronization of stochastic time-delayed memristor-based neural networks via distributed impulsive control

被引：37
作者：
Zhang, Bo [1 ,2 ]
Deng, Feiqi [3 ]
Xie, Shengli [1 ,2 ]
Luo, Shixian [3 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China
[2] Guangdong Key Lab IoT Informat Technol, Guangzhou 510006, Guangdong, Peoples R China
[3] South China Univ Technol, Syst Engn Inst, Guangzhou 510640, Guangdong, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 286卷
基金：
中国国家自然科学基金;
关键词：
Stochastic memristor-based neural networks; Distributed impulsive control; Differential inclusion; COMPLEX DYNAMICAL NETWORKS; VARYING DELAYS; STABILITY; CIRCUITS; DESIGN;
D O I：
10.1016/j.neucom.2018.01.051
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The issue of exponential synchronization of stochastic time-delayed memristor-based neural networks via distributed impulsive control is considered. Based on the characteristics of memristor, the corresponding drive and response stochastic memristor-based neural networks with distributed impulsive control input are established. Then the synchronization error system is gained by using the concept of synchronization and stochastic differential inclusion theory. Through the generalized impulsive delay differential inequality technique, the criteria to guarantee that the error system is exponentially mean square stable, namely that the drive and response systems achieve the exponential synchronization is acquired. At last, the numerical simulation verifies the effectiveness of the obtained theoretical results. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：41 / 50
页数：10
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