A New Settling-time Estimation Protocol to Finite-time Synchronization of Impulsive Memristor-Based Neural Networks

被引:25
|
作者
Wang, Xin [1 ]
Park, Ju H. [2 ]
Yang, Huilan [3 ]
Zhong, Shouming [4 ]
机构
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing Key Lab Nonlinear Circuits & Intelligen, Chongqing 400715, Peoples R China
[2] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea
[3] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China
基金
新加坡国家研究基金会; 中国国家自然科学基金;
关键词
Synchronization; Silicon; Switches; Protocols; Multi-layer neural network; Adaptive synchronization; destabilizing impulses (DIs); memristive neural networks (MNNs); settling time; DYNAMICAL NETWORKS; VARYING DELAY; STABILITY; STABILIZATION;
D O I
10.1109/TCYB.2020.3025932
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, the issues of finite-time synchronization and finite-time adaptive synchronization for the impulsive memristive neural networks (IMNNs) with discontinuous activation functions (DAFs) and hybrid impulsive effects are probed into and elaborated on, where the stabilizing impulses (SIs), inactive impulses (IIs), and destabilizing impulses (DIs) are taken into account, respectively. Not resembling several earlier works, a more extensive range of impulses in the context of impulsive effects has been analyzed without using the known average impulsive interval strategy (AIIS). In light of the theories of differential inclusions and set-valued map, as well as impulsive control, new sufficient criteria with respect to the estimated settling time for synchronization of the related IMNNs are established using two types of switching control approaches, which sufficiently utilize information from not only the SIs, DIs, and DAFs but also the impulse sequences. Two simulation experiments are presented to the efficiency of the proposed results.
引用
收藏
页码:4312 / 4322
页数:11
相关论文
共 50 条
  • [1] Finite-time synchronization of memristor-based neural networks
    Bao, Haibo
    Park, Ju H.
    [J]. 2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 1732 - 1735
  • [2] Multiple finite-time synchronization and settling-time estimation of delayed competitive neural networks
    Wang, Leimin
    Tan, Xingxing
    Wang, Qingyi
    Hu, Junhao
    [J]. NEUROCOMPUTING, 2023, 552
  • [3] Finite-time synchronization of memristor-based neural networks: energy cost estimation
    Lin, Lixiong
    [J]. INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL, 2023, 11 (02) : 738 - 747
  • [4] Finite-time synchronization of memristor-based neural networks: energy cost estimation
    Lixiong Lin
    [J]. International Journal of Dynamics and Control, 2023, 11 : 738 - 747
  • [5] Finite-time synchronization criteria for delayed memristor-based neural networks
    Li, Ning
    Cao, Jinde
    Xiao, Huimin
    [J]. PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, : 3590 - 3594
  • [6] Finite-time synchronization of stochastic memristor-based delayed neural networks
    Shi, Yanchao
    Zhu, Peiyong
    [J]. NEURAL COMPUTING & APPLICATIONS, 2018, 29 (06): : 293 - 301
  • [7] Finite-time synchronization of stochastic memristor-based delayed neural networks
    Yanchao Shi
    Peiyong Zhu
    [J]. Neural Computing and Applications, 2018, 29 : 293 - 301
  • [8] Finite-time synchronization of memristor-based neural networks with mixed delays
    Chen, Chuan
    Li, Lixiang
    Peng, Haipeng
    Yang, Yixian
    Li, Tao
    [J]. NEUROCOMPUTING, 2017, 235 : 83 - 89
  • [9] A new switching control for finite-time synchronization of memristor-based recurrent neural networks
    Gao, Jie
    Zhu, Peiyong
    Alsaedi, Ahmed
    Alsaadi, Fuad E.
    Hayat, Tasawar
    [J]. NEURAL NETWORKS, 2017, 86 : 1 - 9
  • [10] Finite-time synchronization for memristor-based neural networks with time-varying delays
    Abdurahman, Abdujelil
    Jiang, Haijun
    Teng, Zhidong
    [J]. NEURAL NETWORKS, 2015, 69 : 20 - 28