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Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

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  • Wang, Yuxiao
  • Cao, Yuting
  • Guo, Zhenyuan
  • Huang, Tingwen
  • Wen, Shiping

Abstract

This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.

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  • Wang, Yuxiao & Cao, Yuting & Guo, Zhenyuan & Huang, Tingwen & Wen, Shiping, 2020. "Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:apmaco:v:383:y:2020:i:c:s009630032030343x
    DOI: 10.1016/j.amc.2020.125379
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Sha & Bao, Haibo, 2022. "Event-triggered synchronization of coupled memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    2. Zhao, Yong & Ren, Shanshan & Kurths, Jürgen, 2021. "Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    3. Sun, Lin & Su, Lei & Wang, Jing, 2021. "Non-fragile dissipative state estimation for semi-Markov jump inertial neural networks with reaction-diffusion," Applied Mathematics and Computation, Elsevier, vol. 411(C).

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