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
  • [1] A learning- and scenario-based MPC design for nonlinear systems in LPV framework with safety and stability guarantees
    Bao, Yajie
    Abbas, Hossam S.
    Velni, Javad Mohammadpour
    [J]. INTERNATIONAL JOURNAL OF CONTROL, 2024, 97 (07) : 1512 - 1531
  • [2] Bao YJ, 2022, P AMER CONTR CONF, P3260, DOI 10.23919/ACC53348.2022.9867798
  • [3] Path-based multi-sources localization in multiplex networks
    Cheng, Le
    Li, Xianghua
    Han, Zhen
    Luo, Tengyun
    Ma, Lianbo
    Zhu, Peican
    [J]. CHAOS SOLITONS & FRACTALS, 2022, 159
  • [4] Finite-Time and Fixed-Time Synchronization of Delayed Memristive Neural Networks via Adaptive Aperiodically Intermittent Adjustment Strategy
    Cheng, Liyan
    Tang, Fangcheng
    Shi, Xinli
    Chen, Xiangyong
    Qiu, Jianlong
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (11) : 8516 - 8530
  • [5] Pinning Impulsive Synchronization of Complex Networks with Multiple Sizes of Delays via Adaptive Impulsive Intervals
    Ding, Dong
    Tang, Ze
    Wang, Yan
    Ji, Zhicheng
    [J]. CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2021, 40 (09) : 4259 - 4278
  • [6] Pinning synchronization for delayed coupling complex dynamical networks with incomplete transition rates Markovian jump
    Feng, Jianwen
    Cheng, Ke
    Wang, Jingyi
    Deng, Juan
    Zhao, Yi
    [J]. NEUROCOMPUTING, 2021, 434 : 239 - 248
  • [7] Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches
    Fu, Qianhua
    Zhong, Shouming
    Shi, Kaibo
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2021, 402
  • [8] Projective synchronization of fuzzy memristive neural networks with pinning impulsive control
    Fu, Qianhua
    Zhong, Shouming
    Jiang, Wenbo
    Xie, Wenqian
    [J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (15): : 10387 - 10409
  • [9] Finite-time passivity and synchronization of coupled complex-valued memristive neural networks
    Huang, Yanli
    Wu, Fang
    [J]. INFORMATION SCIENCES, 2021, 580 : 775 - 800
  • [10] An memristor-based synapse implementation using BCM learning rule
    Huang, Yongchuang
    Liu, Junxiu
    Harkin, Jim
    McDaid, Liam
    Luo, Yuling
    [J]. NEUROCOMPUTING, 2021, 423 : 336 - 342