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Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks

Author

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  • Jiejie Fan

    (Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
    School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China)

  • Xiaojuan Ban

    (Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
    School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
    Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
    These authors contributed equally to this work.)

  • Manman Yuan

    (School of Computer Science, Inner Mongolia University, Hohhot 010021, China
    National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, Hohhot 010021, China
    These authors contributed equally to this work.)

  • Wenxing Zhang

    (School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
    School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

Abstract

To reduce the communication and computation overhead of neural networks, a novel pinning event-triggered scheme (PETS) is developed in this paper, which enables pinning synchronization of uncertain coupled memristive neural networks (CMNNs) under limited resources. Time-varying delays, uncertainties, and mismatched parameters are all considered, which makes the system more interpretable. In addition, from the low energy cost point of view, an algorithm for pinned node selection is designed to further investigate the newly event-triggered function under limited communication resources. Meanwhile, based on the PETS and following the Lyapunov functional method, sufficient conditions for the pinning exponential stability of the proposed coupled error system are formulated, and the analysis of the self-triggered method shows that our method can efficiently avoid Zeno behavior under the newly determined triggered conditions, which contribute to better PETS performance. Extensive experiments demonstrate that the PETS significantly outperforms the existing schemes in terms of solution quality.

Suggested Citation

  • Jiejie Fan & Xiaojuan Ban & Manman Yuan & Wenxing Zhang, 2024. "Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks," Mathematics, MDPI, vol. 12(6), pages 1-28, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:821-:d:1355021
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    References listed on IDEAS

    as
    1. Wang, Weiping & Sun, Yue & Yuan, Manman & Wang, Zhen & Cheng, Jun & Fan, Denggui & Kurths, Jürgen & Luo, Xiong & Wang, Chunyang, 2021. "Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
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