Switching synchronization of reaction-diffusion neural networks with time-varying delays

被引:7
|
作者
Hu, Dandan [1 ]
Tan, Jieqing [1 ,2 ]
Shi, Kaibo [3 ]
Ding, Kui [4 ]
机构
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[2] Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China
[3] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China
[4] Hunan Normal Univ, Sch Math & Stat, MOE LCSM, Changsha 410081, Peoples R China
基金
中国国家自然科学基金;
关键词
Switching reaction-diffusion neural; networks; Synchronization; Switching-time-dependent Lyapunov; functional; function; Switching law strategies; Time-varying delays; STABILITY ANALYSIS; STABILIZATION; SYSTEMS;
D O I
10.1016/j.chaos.2021.111766
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This paper explores the switching synchronization problem of reaction-diffusion neural networks with time-varying delays, and two improved synchronization switching law strategies are proposed for stability analysis. One is constructed by adopting a Lyapunov-Krasovskii functional combined with the use of improved Wirtinger's integral inequality for managing the reaction-diffusion terms. The other is designed to utilize the Lyapunov-Razumikhin function, which is easier to deal with the reaction-diffusion terms directly compared to the former one. As a result, the time-space feature of the proposed switching synchronization is more robust and compatible than previous works. Finally, the simulated numerical experiments make out the effectiveness of the developed approaches in this work.(c) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Synchronization of Reaction-Diffusion Neural Networks with Mixed Time-Varying Delays
    Wu H.
    Zhang X.
    Li R.
    Yao R.
    Journal of Control, Automation and Electrical Systems, 2014, 26 (01) : 16 - 27
  • [2] Pinning Impulsive Synchronization of Reaction-Diffusion Neural Networks With Time-Varying Delays
    Liu, Xinzhi
    Zhang, Kexue
    Xie, Wei-Chau
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2017, 28 (05) : 1055 - 1067
  • [3] Synchronization of Multiple Reaction-Diffusion Neural Networks With Heterogeneous and Unbounded Time-Varying Delays
    Zhang, Hao
    Zeng, Zhigang
    Han, Qing-Long
    IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (08) : 2980 - 2991
  • [4] Stability and Synchronization of Nonautonomous Reaction-Diffusion Neural Networks With General Time-Varying Delays
    Zhang, Hao
    Zeng, Zhigang
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (10) : 5804 - 5817
  • [5] Adaptive synchronization of memristive neural networks with time-varying delays and reaction-diffusion term
    Tu, Zhengwen
    Ding, Nan
    Li, Liangliang
    Feng, Yuming
    Zou, Limin
    Zhang, Wei
    APPLIED MATHEMATICS AND COMPUTATION, 2017, 311 : 118 - 128
  • [6] Synchronization of Fractional Reaction-Diffusion Neural Networks With Time-Varying Delays and Input Saturation
    Wang, Yin
    Liu, Shutang
    Wu, Xiang
    IEEE ACCESS, 2021, 9 : 50907 - 50916
  • [7] Asymptotic synchronization of a class of neural networks with reaction-diffusion terms and time-varying delays
    Lou, Xu-Yang
    Cui, Bao-Tong
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2006, 52 (6-7) : 897 - 904
  • [8] Synchronization for impulsive hybrid-coupled reaction-diffusion neural networks with time-varying delays
    Wu, Kaixiong
    Li, Bing
    Du, Yuwei
    Du, Shishi
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2020, 82
  • [9] Synchronization for a class of generalized neural networks with interval time-varying delays and reaction-diffusion terms
    Gan, Qintao
    Liu, Tielin
    Liu, Chang
    Lv, Tianshi
    NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2016, 21 (03): : 379 - 399
  • [10] Pinning Synchronization of Coupled Neural Networks With Multiple Time-Varying Delays and Reaction-Diffusion Terms
    Xu Meng
    Wang Jin-Liang
    Huang Yan-Li
    Wang Shu-Xue
    Ren Shun-Yan
    PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 7267 - 7272