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
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共 44 条
  • [31] Pinning synchronization and adaptive synchronization of complex-valued inertial neural networks with time-varying delays in fixed-time interval
    Yu, Yaning
    Zhang, Ziye
    Zhong, Maiying
    Wang, Zhen
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2022, 359 (02): : 1434 - 1456
  • [32] Projective quasi-synchronization of coupled memristive neural networks with uncertainties and impulsive effect
    Yuan, Manman
    Luo, Xiong
    Hu, Jun
    Wang, Songxin
    [J]. FRONTIERS IN NEUROROBOTICS, 2022, 16
  • [33] Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding
    Yuan, Manman
    Luo, Xiong
    Mao, Xue
    Han, Zhen
    Sun, Lei
    Zhu, Peican
    [J]. CHAOS SOLITONS & FRACTALS, 2022, 161
  • [34] Exponential Synchronization of Delayed Memristor-Based Uncertain Complex-Valued Neural Networks for Image Protection
    Yuan, Manman
    Wang, Weiping
    Wang, Zhen
    Luo, Xiong
    Kurths, Juergen
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (01) : 151 - 165
  • [35] Pinning Synchronization of Coupled Memristive Recurrent Neural Networks with Mixed Time-Varying Delays and Perturbations
    Yuan, Manman
    Luo, Xiong
    Wang, Weiping
    Li, Lixiang
    Peng, Haipeng
    [J]. NEURAL PROCESSING LETTERS, 2019, 49 (01) : 239 - 262
  • [36] Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays
    Yuan, Manman
    Wang, Weiping
    Luo, Xiong
    Liu, Linlin
    Zhao, Wenbing
    [J]. CHAOS SOLITONS & FRACTALS, 2018, 113 : 244 - 260
  • [37] Pinning Event-Triggered Sampling Control for Synchronization of T-S Fuzzy Complex Networks With Partial and Discrete-Time Couplings
    Zhang, Ruimei
    Zeng, Deqiang
    Park, Ju H.
    Liu, Yajuan
    Zhong, Shouming
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2019, 27 (12) : 2368 - 2380
  • [38] Event-triggered hybrid impulsive control for synchronization of memristive neural networks
    Zhang, Yijun
    Bao, Yuangui
    [J]. SCIENCE CHINA-INFORMATION SCIENCES, 2020, 63 (05)
  • [39] Cluster Synchronization of Coupled Neural Networks With Levy Noise via Event-Triggered Pinning Control
    Zhou, Wuneng
    Sun, Yuqing
    Zhang, Xin
    Shi, Peng
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (11) : 6144 - 6157
  • [40] Pinning synchronization of delayed complex networks under self-triggered control
    Zhou, Xiaotao
    Li, Lulu
    Zhao, Xiao-Wen
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2021, 358 (02): : 1599 - 1618