Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks

被引:0
|
作者
Fan, Jiejie [1 ,2 ]
Ban, Xiaojuan [1 ,2 ,3 ]
Yuan, Manman [4 ,5 ]
Zhang, Wenxing [6 ,7 ]
机构
[1] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Sch Intelligence Sci & Technol, Beijing 100083, Peoples R China
[3] Univ Sci & Technol Beijing, Key Lab Intelligent Unmanned Syst Bion, Minist Educ, Beijing 100083, Peoples R China
[4] Inner Mongolia Univ, Sch Comp Sci, Hohhot 010021, Peoples R China
[5] Natl & Local Joint Engn Res Ctr Intelligent Inform, Hohhot 010021, Peoples R China
[6] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing 100083, Peoples R China
[7] Inner Mongolia Univ Sci & Technol, Sch Mech Engn, Baotou 014010, Peoples R China
基金
中国国家自然科学基金;
关键词
event-triggered mechanism; memristor; Zeno behavior; synchronization; pinning control; COMPLEX DYNAMICAL NETWORKS; EXPONENTIAL SYNCHRONIZATION; QUASI-SYNCHRONIZATION; TIME; SYSTEMS;
D O I
10.3390/math12060821
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
To reduce the communication and computation overhead of neural networks, a novel pinning event-triggered scheme (PETS) is developed in this paper, which enables pinning synchronization of uncertain coupled memristive neural networks (CMNNs) under limited resources. Time-varying delays, uncertainties, and mismatched parameters are all considered, which makes the system more interpretable. In addition, from the low energy cost point of view, an algorithm for pinned node selection is designed to further investigate the newly event-triggered function under limited communication resources. Meanwhile, based on the PETS and following the Lyapunov functional method, sufficient conditions for the pinning exponential stability of the proposed coupled error system are formulated, and the analysis of the self-triggered method shows that our method can efficiently avoid Zeno behavior under the newly determined triggered conditions, which contribute to better PETS performance. Extensive experiments demonstrate that the PETS significantly outperforms the existing schemes in terms of solution quality.
引用
收藏
页数:28
相关论文
共 50 条
  • [1] Synchronization of Fractional-Order Uncertain Delayed Neural Networks with an Event-Triggered Communication Scheme
    Hymavathi, M.
    Ali, M. Syed
    Ibrahim, Tarek F.
    Younis, B. A.
    Osman, Khalid, I
    Mukdasai, Kanit
    [J]. FRACTAL AND FRACTIONAL, 2022, 6 (11)
  • [2] Event-triggered synchronization of coupled memristive neural networks
    Zhu, Sha
    Bao, Haibo
    [J]. Applied Mathematics and Computation, 2022, 415
  • [3] Event-triggered synchronization of coupled memristive neural networks
    Zhu, Sha
    Bao, Haibo
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2022, 415
  • [4] Event-Triggered Bipartite Synchronization of Delayed Inertial Memristive Neural Networks With Unknown Disturbances
    Liu, Xiaoyang
    He, Haibin
    Cao, Jinde
    [J]. IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2024, 11 (03): : 1408 - 1419
  • [5] Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme
    Vadivel, R.
    Hammachukiattikul, P.
    Gunasekaran, Nallappan
    Saravanakumar, R.
    Dutta, Hemen
    [J]. CHAOS SOLITONS & FRACTALS, 2021, 150
  • [6] Event-triggered finite-time quantized synchronization of uncertain delayed neural networks
    Zhang, Yingqi
    Li, Xiao
    Liu, Caixia
    [J]. OPTIMAL CONTROL APPLICATIONS & METHODS, 2022, 43 (06): : 1584 - 1603
  • [7] Synchronization control of quaternion-valued memristive neural networks with and without event-triggered scheme
    Wei, Ruoyu
    Cao, Jinde
    [J]. COGNITIVE NEURODYNAMICS, 2019, 13 (05) : 489 - 502
  • [8] Synchronization control of quaternion-valued memristive neural networks with and without event-triggered scheme
    Ruoyu Wei
    Jinde Cao
    [J]. Cognitive Neurodynamics, 2019, 13 : 489 - 502
  • [9] Event-triggered hybrid impulsive control for synchronization of memristive neural networks
    Yijun ZHANG
    Yuangui BAO
    [J]. Science China(Information Sciences), 2020, 63 (05) : 75 - 86
  • [10] Event-triggered hybrid impulsive control for synchronization of memristive neural networks
    Zhang, Yijun
    Bao, Yuangui
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (05)