Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay

被引：87
|
作者：
Chen, Chuan [1 ]
Li, Lixiang [1 ]
Peng, Haipeng [1 ]
Yang, Yixian [1 ,2 ]
机构：
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China
[2] State Key Lab Publ Big Data, Guiyang 550025, Guizhou, Peoples R China
来源：
NEURAL NETWORKS | 2017年 / 96卷
基金：
中国国家自然科学基金;
关键词：
Fixed-time synchronization; Memristor; BAM neural networks; Delays; EXPONENTIAL STABILITY; SYSTEMS; STABILIZATION; COMMUNICATION; EXISTENCE;
D O I：
10.1016/j.neunet.2017.08.012
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 is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：47 / 54
页数：8
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