Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty

被引：82
|
作者：
Gu, Yajuan [1 ]
Yu, Yongguang [1 ]
Wang, Hu [2 ]
机构：
[1] Beijing Jiaotong Univ, Dept Math, Beijing 100044, Peoples R China
[2] Cent Univ Finance & Econ, Sch Stat & Math, Beijing 100081, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2016年 / 353卷 / 15期
关键词：
STABILITY ANALYSIS; PROJECTIVE SYNCHRONIZATION; DYNAMIC-ANALYSIS; DESIGN;
D O I：
10.1016/j.jfranklin.2016.06.029
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, the global synchronization for fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty is investigated. A comparison theorem for a class of fractional order multiple time-delayed systems is given. Under the framework of Filippov solution and differential inclusion theory, the synchronization conditions of fractional-order multiple time-delayed memristor-based neural networks with parameter uncertainty are derived, based on the given comparison theorem and Lyapunov method. Furthermore, the global asymptotical stability conditions of fractional-order multiple time-delayed memristor-based neural networks are obtained. Finally, two numerical examples are presented to show the effectiveness of our theoretical results. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：3657 / 3684
页数：28
相关论文
共 50 条
  • [1] Stability and synchronization of memristor-based fractional-order delayed neural networks
    Chen, Liping
    Wu, Ranchao
    Cao, Jinde
    Liu, Jia-Bao
    NEURAL NETWORKS, 2015, 71 : 37 - 44
  • [2] Synchronization of memristor-based delayed BAM neural networks with fractional-order derivatives
    Rajivganthi, Chinnathambi
    Rihan, Fathalla A.
    Lakshmanan, Shanmugam
    Rakkiyappan, Rajan
    Muthukumar, Palanisamy
    COMPLEXITY, 2016, 21 (S2) : 412 - 426
  • [3] Projective synchronization of fractional-order memristor-based neural networks
    Bao, Hai-Bo
    Cao, Jin-De
    NEURAL NETWORKS, 2015, 63 : 1 - 9
  • [4] Projective synchronization for fractional-order memristor-based neural networks with time delays
    Yajuan Gu
    Yongguang Yu
    Hu Wang
    Neural Computing and Applications, 2019, 31 : 6039 - 6054
  • [5] Projective synchronization for fractional-order memristor-based neural networks with time delays
    Gu, Yajuan
    Yu, Yongguang
    Wang, Hu
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (10): : 6039 - 6054
  • [6] Adaptive synchronization of fractional-order memristor-based neural networks with time delay
    Haibo Bao
    Ju H. Park
    Jinde Cao
    Nonlinear Dynamics, 2015, 82 : 1343 - 1354
  • [7] Adaptive synchronization of fractional-order memristor-based neural networks with time delay
    Bao, Haibo
    Park, Ju H.
    Cao, Jinde
    NONLINEAR DYNAMICS, 2015, 82 (03) : 1343 - 1354
  • [8] Robust synchronization of memristor-based fractional-order Hopfield neural networks with parameter uncertainties
    Shuxin Liu
    Yongguang Yu
    Shuo Zhang
    Neural Computing and Applications, 2019, 31 : 3533 - 3542
  • [9] Robust synchronization of memristor-based fractional-order Hopfield neural networks with parameter uncertainties
    Liu, Shuxin
    Yu, Yongguang
    Zhang, Shuo
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (08): : 3533 - 3542
  • [10] Fixed-Time Synchronization of Delayed Fractional-Order Memristor-Based Fuzzy Cellular Neural Networks
    Sun, Yeguo
    Liu, Yihong
    IEEE ACCESS, 2020, 8 : 165951 - 165962
12345