Projective synchronization for fractional-order memristor-based neural networks with time delays

被引：44
|
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：
NEURAL COMPUTING & APPLICATIONS | 2019年 / 31卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Memristor-based neural networks; Synchronization; Fractional-order; Time delays; STABILITY ANALYSIS; EXPONENTIAL SYNCHRONIZATION; ADAPTIVE-CONTROL; CHAOTIC SYSTEM; DYNAMICS; DESIGN;
D O I：
10.1007/s00521-018-3391-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the global projective synchronization for fractional-order memristor-based neural networks with multiple time delays is investigated via combining open loop control with the time-delayed feedback control. A comparison theorem for a class of fractional-order systems with multiple time delays is proposed. Based on the given comparison theorem and Lyapunov method, the synchronization conditions are derived under the framework of Filippov solution and differential inclusion theory. Several feedback control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization for the fractional-order memristor-based neural networks with time delays. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.
引用
收藏
页码：6039 / 6054
页数：16
相关论文
共 50 条
  • [31] Pinning synchronization of fractional-order memristor-based neural networks with multiple time-varying delays via static or dynamic coupling
    Jia, Jia
    Zeng, Zhigang
    Wang, Fei
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2021, 358 (01): : 895 - 933
  • [32] Novel results on projective synchronization of fractional-order neural networks with multiple time delays
    Zhang, Weiwei
    Cao, Jinde
    Wu, Ranchao
    Chen, Dingyuan
    Alsaadi, Fuad E.
    CHAOS SOLITONS & FRACTALS, 2018, 117 : 76 - 83
  • [33] Global Stabilization of Fractional-Order Memristor-Based Neural Networks With Time Delay
    Jia, Jia
    Huang, Xia
    Li, Yuxia
    Cao, Jinde
    Alsaedi, Ahmed
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (03) : 997 - 1009
  • [34] Dynamical analysis of memristor-based fractional-order neural networks with time delay
    Cui, Xueli
    Yu, Yongguang
    Wang, Hu
    Hu, Wei
    MODERN PHYSICS LETTERS B, 2016, 30 (18):
  • [35] Global attractivity of memristor-based fractional-order neural networks
    Zhang, Shuo
    Yu, Yongguang
    Gu, Yajuan
    NEUROCOMPUTING, 2017, 227 : 64 - 73
  • [36] Finite-time synchronization of delayed fractional-order quaternion-valued memristor-based neural networks
    Ding, Dawei
    You, Ziruo
    Hu, Yongbing
    Yang, Zongli
    Ding, Lianghui
    INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2021, 35 (03):
  • [37] Bipartite Synchronization of Fractional-Order Memristor-Based Coupled Delayed Neural Networks with Pinning Control
    Dhivakaran, P. Babu
    Vinodkumar, A.
    Vijay, S.
    Lakshmanan, S.
    Alzabut, J.
    El-Nabulsi, R. A.
    Anukool, W.
    MATHEMATICS, 2022, 10 (19)
  • [38] Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks
    Chen, Jiejie
    Zeng, Zhigang
    Jiang, Ping
    NEURAL NETWORKS, 2014, 51 : 1 - 8
  • [39] Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays
    Abdujelil Abdurahman
    Haijun Jiang
    Kaysar Rahman
    Cognitive Neurodynamics, 2015, 9 : 603 - 613
  • [40] Global Mittag-Leffler synchronization of discontinuous memristor-based fractional-order fuzzy inertial neural networks with mixed delays
    Pan, Haihe
    Huang, Chengdai
    Cao, Jinde
    Liu, Heng
    NEUROCOMPUTING, 2025, 626
12345