Practical finite-time synchronization of delayed fuzzy cellular neural networks with fractional-order

被引:10
|
作者
Du, Feifei [1 ]
Lu, Jun-Guo [2 ,3 ,4 ]
Zhang, Qing-Hao [2 ,3 ,4 ]
机构
[1] Northwest A&F Univ, Coll Sci, Xianyang 712100, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
[3] Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China
[4] Shanghai Engn Res Ctr Intelligent Control & Manage, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Fuzzy neural network; Fractional-order; Practical finite-time synchronization; Delay; STABILITY;
D O I
10.1016/j.ins.2024.120457
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The practical finite -time (PFT) synchronization of fractional -order delayed fuzzy cellular neural networks (FODFCNNs) is presented in this article. Initially, a useful practical finite time (FT) stable lemma is developed, serving as an efficient instrument for the PFT synchronization of fractional -order systems. Subsequently, a new PFT synchronization criterion for FODFCNNs is derived using the designed controller and the aforementioned lemma. Simultaneously, the settling time for PFT synchronization is determined, relying on specific controller parameters and the initial conditions of the considered systems. Ultimately, the accuracy of the derived outcomes is confirmed through a numerical simulation.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Finite-Time Cluster Synchronization of Delayed Fractional-Order Fully Complex-Valued Community Networks
    Kang, Qiaokun
    Yang, Qingxi
    Lin, Zhilong
    Gan, Qintao
    IEEE ACCESS, 2022, 10 : 103948 - 103962
  • [22] Practical Finite-Time Synchronization of Fractional-Order Complex Dynamical Networks With Application to Lorenz's Circuit
    Wei, Chen
    Wang, Xiaoping
    Lai, Jingang
    Zeng, Zhigang
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2024,
  • [23] Finite-time synchronization of fully complex-valued neural networks with fractional-order
    Zheng, Bibo
    Hu, Cheng
    Yu, Juan
    Jiang, Haijun
    NEUROCOMPUTING, 2020, 373 : 70 - 80
  • [24] Finite-time synchronization control of fractional-order memristive neural networks with time varying delays
    Liu, Yihong
    Sun, Yeguo
    PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 3231 - 3237
  • [25] Novel methods of finite-time synchronization of fractional-order delayed memristor-based Cohen-Grossberg neural networks
    Du, Feifei
    Lu, Jun-Guo
    NONLINEAR DYNAMICS, 2023, 111 (20) : 18985 - 19001
  • [26] Asymptotic and Finite-Time Cluster Synchronization of Coupled Fractional-Order Neural Networks With Time Delay
    Liu, Peng
    Zeng, Zhigang
    Wang, Jun
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (11) : 4956 - 4967
  • [27] Further results on distributed finite-time synchronization for fractional-order coupled neural networks
    Zheng, Bibo
    Dai, Wei
    Wang, Zhanshan
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2025, 147
  • [28] Fixed-Time Synchronization of Delayed Fractional-Order Memristor-Based Fuzzy Cellular Neural Networks
    Sun, Yeguo
    Liu, Yihong
    IEEE ACCESS, 2020, 8 : 165951 - 165962
  • [29] Finite-Time Synchronization for Fractional Order Fuzzy Inertial Cellular Neural Networks with Piecewise Activations and Mixed Delays
    Liu, Yihong
    Sun, Yeguo
    FRACTAL AND FRACTIONAL, 2023, 7 (04)
  • [30] Delay-dependent finite-time synchronization criterion of fractional-order delayed complex networks
    Du, Feifei
    Lu, Jun-Guo
    Zhang, Qing-Hao
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2023, 119