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

被引：88
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
相关论文
共 43 条
  • [1] Finite-time synchronization for memristor-based neural networks with time-varying delays
    Abdurahman, Abdujelil
    Jiang, Haijun
    Teng, Zhidong
    [J]. NEURAL NETWORKS, 2015, 69 : 20 - 28
  • [2] Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations
    Anbuvithya, R.
    Mathiyalagan, K.
    Sakthivel, R.
    Prakash, P.
    [J]. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2015, 29 (1-3) : 427 - 440
  • [3] Fixed-time synchronization of delayed memristor-based recurrent neural networks
    Cao, Jinde
    Li, Ruoxia
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2017, 60 (03)
  • [4] Chen C., 2017, NEURAL PROCESSING LE
  • [5] Finite-time synchronization of memristor-based neural networks with mixed delays
    Chen, Chuan
    Li, Lixiang
    Peng, Haipeng
    Yang, Yixian
    Li, Tao
    [J]. NEUROCOMPUTING, 2017, 235 : 83 - 89
  • [6] MEMRISTOR - MISSING CIRCUIT ELEMENT
    CHUA, LO
    [J]. IEEE TRANSACTIONS ON CIRCUIT THEORY, 1971, CT18 (05): : 507 - +
  • [7] Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions
    Ding, Xiaoshuai
    Cao, Jinde
    Alsaedi, Ahmed
    Alsaadi, Fuad E.
    Hayat, Tasawar
    [J]. NEURAL NETWORKS, 2017, 90 : 42 - 55
  • [8] Global asymptotic stability of Markovian jumping stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays
    Du, Yuanhua
    Zhong, Shouming
    Zhou, Nan
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2014, 243 : 624 - 636
  • [9] Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays
    Guo, Runan
    Zhang, Ziye
    Liu, Xiaoping
    Lin, Chong
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2017, 311 : 100 - 117
  • [10] Adaptive exponential synchronization of memristive neural networks with mixed time-varying delays
    Han, Xiaoming
    Wu, Huaiqin
    Fang, Bolin
    [J]. NEUROCOMPUTING, 2016, 201 : 40 - 50
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