Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks

被引：123
|
作者：
Zheng, Mingwen [1 ,2 ]
Li, Lixiang [3 ]
Peng, Haipeng [3 ]
Xiao, Jinghua [1 ,4 ]
Yang, Yixian [3 ]
Zhang, Yanping [2 ]
Zhao, Hui [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Shandong Univ Technol, Sch Sci, Zibo 255000, Peoples R China
[3] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China
[4] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
来源：
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2018年 / 59卷
基金：
中国国家自然科学基金;
关键词：
Finite-time stability; MFFCNN; Gronwall-Bellman inequality; Linear feedback controller; EXPONENTIAL LAG SYNCHRONIZATION; TURBINE GOVERNING SYSTEM; MITTAG-LEFFLER STABILITY; PROJECTIVE SYNCHRONIZATION; ASSOCIATIVE MEMORY; NONLINEAR DYNAMICS; EXISTENCE;
D O I：
10.1016/j.cnsns.2017.11.025
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：272 / 291
页数：20
相关论文
共 50 条
  • [41] Finite-time synchronization of memristor-based neural networks with mixed delays
    Chen, Chuan
    Li, Lixiang
    Peng, Haipeng
    Yang, Yixian
    Li, Tao
    NEUROCOMPUTING, 2017, 235 : 83 - 89
  • [42] Stability analysis of memristor-based time-delay fractional-order neural networks
    Liu, Weizhen
    Jiang, Minghui
    Yan, Meng
    NEUROCOMPUTING, 2019, 323 : 117 - 127
  • [43] Synchronization for fractional-order time-delayed memristor-based neural networks with parameter uncertainty
    Gu, Yajuan
    Yu, Yongguang
    Wang, Hu
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (15): : 3657 - 3684
  • [44] Synchronization of memristor-based delayed BAM neural networks with fractional-order derivatives
    Rajivganthi, Chinnathambi
    Rihan, Fathalla A.
    Lakshmanan, Shanmugam
    Rakkiyappan, Rajan
    Muthukumar, Palanisamy
    COMPLEXITY, 2016, 21 (S2) : 412 - 426
  • [45] Finite-Time Stability of Fractional-Order Neural Networks with Delay
    吴然超
    黑鑫东
    陈立平
    Communications in Theoretical Physics, 2013, 60 (08) : 189 - 193
  • [46] Finite-Time Stability of Fractional-Order Neural Networks with Delay
    Wu Ran-Chao
    Hei Xin-Dong
    Chen Li-Ping
    COMMUNICATIONS IN THEORETICAL PHYSICS, 2013, 60 (02) : 189 - 193
  • [47] Global Finite-time Stability for Fractional-order Neural Networks
    Hu, Xiaolong
    OPTICAL MEMORY AND NEURAL NETWORKS, 2020, 29 (02) : 77 - 99
  • [48] Global Finite-time Stability for Fractional-order Neural Networks
    Optical Memory and Neural Networks, 2020, 29 : 77 - 99
  • [49] Finite-time synchronization control of a class of memristor-based recurrent neural networks
    Jiang, Minghui
    Wang, Shuangtao
    Mei, Jun
    Shen, Yanjun
    NEURAL NETWORKS, 2015, 63 : 133 - 140
  • [50] Stability of Memristor-based Fractional-order Neural Networks with Mixed Time-delay and Impulsive
    Ji Chen
    Minghui Jiang
    Neural Processing Letters, 2023, 55 : 4697 - 4718
12345