Fixed-time synchronization of quaternion-valued neural networks with impulsive effects: A non-decomposition method

被引:12
作者
Peng, Tao [1 ,2 ]
Lu, Jianquan
Xiong, Jiang [1 ]
Tu, Zhengwen [1 ]
Liu, Yang [3 ]
Lou, Jungang [4 ]
机构
[1] Chongqing Three Gorges Univ, Sch Math & Stat, Chongqing 404100, Peoples R China
[2] Southeast Univ, Sch Math, Dept Syst Sci, Nanjing 210096, Peoples R China
[3] Zhejiang Normal Univ, Sch Math Sci, Jinhua 321004, Peoples R China
[4] Huzhou Univ, Yangtze Delta Reg Huzhou Inst Intelligent Transpor, Huzhou 313000, Peoples R China
来源
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2024年 / 132卷
基金
中国国家自然科学基金;
关键词
Fixed-time synchronization; Impulse; Quaternion-valued neural networks; FINITE-TIME; DELAYS; STABILIZATION; STABILITY; SYSTEMS;
D O I
10.1016/j.cnsns.2024.107865
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper studies the fixed -time synchronization of a class of quaternion-valued neural networks (QVNNs) with time delays and impulses. In contrast to some existing decomposition methods for studying this problem, this paper presents several strategies to simplify the implicit Lyapunov function method from both controller and implicit function equation perspectives. The benefits of these strategies are, in the first strategy, the controller does not contain the Lyapunov function so that the controller will be more general, and in the second strategy, the implicit function equation is simpler so that the restriction on the bounded Lyapunov function can be dispensed with, which also makes the technique of handling time delays simpler. Furthermore, three sufficient conditions for fixed -time synchronization of the above QVNN are presented, despite the impulsive influence. Finally, we verify the feasibility of our methods with three examples.
引用
收藏
页数:19
相关论文
共 40 条
  • [1] Soft variable-structure controls: a survey
    Adamy, J
    Flemming, A
    [J]. AUTOMATICA, 2004, 40 (11) : 1821 - 1844
  • [2] Global Mittag-Leffler stability analysis of impulsive fractional-order complex-valued BAM neural networks with time varying delays
    Ali, M. Syed
    Narayanan, G.
    Shekher, Vineet
    Alsaedi, Ahmed
    Ahmad, Bashir
    [J]. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2020, 83
  • [3] Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication
    Alimi, Adel M.
    Aouiti, Chaouki
    Assali, El Abed
    [J]. NEUROCOMPUTING, 2019, 332 : 29 - 43
  • [4] Finite-Time and Fixed-Time Synchronization of Inertial Cohen-Grossberg-Type Neural Networks with Time Varying Delays
    Aouiti, Chaouki
    Assali, El Abed
    El Foutayeni, Youssef
    [J]. NEURAL PROCESSING LETTERS, 2019, 50 (03) : 2407 - 2436
  • [5] Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties
    Chen, Xiaofeng
    Li, Zhongshan
    Song, Qiankun
    Hu, Jin
    Tan, Yuanshun
    [J]. NEURAL NETWORKS, 2017, 91 : 55 - 65
  • [6] Finite-time stabilization for delaye d quaternion-value d coupled neural networks with saturated impulse
    Chen, Yuan
    Wu, Jianwei
    Bao, Haibo
    [J]. APPLIED MATHEMATICS AND COMPUTATION, 2022, 425
  • [7] Cui YD, 2013, 2013 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION (SII), P527, DOI 10.1109/SII.2013.6776617
  • [8] Quaternion Neural Networks Applied to Prostate Cancer Gleason Grading
    Greenblatt, Aaron
    Mosquera-Lopez, Clara
    Agaian, Sos
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2013), 2013, : 1144 - 1149
  • [9] Experimental observation of non-Abelian topological charges and edge states
    Guo, Qinghua
    Jiang, Tianshu
    Zhang, Ruo-Yang
    Zhang, Lei
    Zhang, Zhao-Qing
    Yang, Biao
    Zhang, Shuang
    Chan, C. T.
    [J]. NATURE, 2021, 594 (7862) : 195 - +
  • [10] Quantized adaptive pinning control for fixed/preassigned-time cluster synchronization of multi-weighted complex networks with stochastic disturbances
    He, Qiushi
    Ma, Yuechao
    [J]. NONLINEAR ANALYSIS-HYBRID SYSTEMS, 2022, 44