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
  • [11] Synchronization of drive-response networks with event-based pinning control
    Jia, Qiang
    Bram, Andrew K.
    Han, Zeyu
    [J]. NEURAL COMPUTING & APPLICATIONS, 2021, 33 (14) : 8649 - 8658
  • [12] Synchronization of multiplex networks with stochastic perturbations via pinning adaptive control *
    Jin, Xin
    Wang, Zhengxin
    Yang, Huihui
    Song, Qiang
    Xiao, Min
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2021, 358 (07): : 3994 - 4012
  • [13] Finite-time and fixed-time synchronization analysis of shunting inhibitory memristive neural networks with time-varying delays
    Kashkynbayev, Ardak
    Issakhanov, Alfarabi
    Otkel, Madina
    Kurths, Juergen
    [J]. CHAOS SOLITONS & FRACTALS, 2022, 156
  • [14] Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control
    Li, Xiaofan
    Fang, Jian-an
    Li, Huiyuan
    [J]. NEURAL NETWORKS, 2017, 93 : 165 - 175
  • [15] Anti-Synchronization of Discrete-Time Fuzzy Memristive Neural Networks via Impulse Sampled-Data Communication
    Liu, Fen
    Meng, Wei
    Lu, Renquan
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (07) : 4122 - 4133
  • [16] Optimizing Pinning Control of Complex Dynamical Networks Based on Spectral Properties of Grounded Laplacian Matrices
    Liu, Hui
    Xu, Xuanhong
    Lu, Jun-An
    Chen, Guanrong
    Zeng, Zhigang
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (02): : 786 - 796
  • [17] Event-triggered synchronization in fixed time for complex dynamical networks with discontinuous nodes and disturbances
    Liu, Jie
    Wu, Huaiqin
    Cao, Jinde
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 38 (03) : 2503 - 2515
  • [18] Dynamic event-triggered approach for cluster synchronization of complex dynamical networks with switching via pinning control
    Liu, Ling
    Zhou, Wuneng
    Li, Xiaoli
    Sun, Yuqing
    [J]. NEUROCOMPUTING, 2019, 340 : 32 - 41
  • [19] Quasi-Synchronization of Heterogeneous Networks With a Generalized Markovian Topology and Event-Triggered Communication
    Liu, Xinghua
    Tay, Wee Peng
    Liu, Zhi-Wei
    Xiao, Gaoxi
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2020, 50 (10) : 4200 - 4213
  • [20] Pinning multisynchronization of delayed fractional-order memristor-based neural networks with nonlinear coupling and almost-periodic perturbations
    Peng, Libiao
    Li, Xifeng
    Bi, Dongjie
    Xie, Xuan
    Xie, Yongle
    [J]. NEURAL NETWORKS, 2021, 144 : 372 - 383