Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays

被引：30

作者：

Yuan, Manman ^{[1
,2
]}

Wang, Weiping ^{[1
,2
,3
]}

Luo, Xiong ^{[1
,2
]}

Liu, Linlin ^{[4
]}

Zhao, Wenbing ^{[5
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China

[2] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China

[3] Humboldt Univ, Inst Phys, D-10099 Berlin, Germany

[4] Beijing Univ Technol, Inst Microstruct & Properties Adv Mat, Beijing 100124, Peoples R China

[5] Cleveland State Univ, Dept Elect Engn & Comp Sci, Cleveland, OH 44115 USA

来源：

CHAOS SOLITONS & FRACTALS | 2018年 / 113卷

基金：

中国国家自然科学基金;

关键词：

Memristor; Stochastic BAM neural networks; Finite-time anti-synchronization; Probabilistic time-varying delays; Leakage delays; EXPONENTIAL SYNCHRONIZATION; IMPULSIVE CONTROL; STABILITY; SYSTEM;

D O I：

10.1016/j.chaos.2018.06.013

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：244 / 260

页数：17

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