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Event-Triggered Synchronization of Coupled Neural Networks with Reaction–Diffusion Terms

Author

Listed:
  • Abulajiang Aili

    (School of Mathematics and Statistics, Kashi University, Kashi 844006, China)

  • Shenglong Chen

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830017, China)

  • Sibao Zhang

    (School of Mathematics and Statistics, Kashi University, Kashi 844006, China)

Abstract

This paper focuses on the event-triggered synchronization of coupled neural networks with reaction–diffusion terms. At first, an effective event-triggered controller was designed based on time sampling. It is worth noting that the data of the controller for this type can be updated only when corresponding triggering conditions are satisfied, which can significantly reduce the communication burden of the control systems compared to other control strategies. Furthermore, some sufficient criteria were obtained to ensure the event-triggered synchronization of the considered systems through the use of an inequality techniques as well as the designed controller. Finally, the validity of the theoretical results was confirmed using numerical examples.

Suggested Citation

  • Abulajiang Aili & Shenglong Chen & Sibao Zhang, 2024. "Event-Triggered Synchronization of Coupled Neural Networks with Reaction–Diffusion Terms," Mathematics, MDPI, vol. 12(9), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1409-:d:1388636
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    References listed on IDEAS

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    1. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    2. Luo, Lingao & Li, Lulu & Huang, Wei, 2024. "Asymptotic stability of fractional-order Hopfield neural networks with event-triggered delayed impulses and switching effects," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 491-504.
    3. Mao, Kun & Liu, Xiaoyang & Cao, Jinde & Hu, Yuanfa, 2022. "Finite-time bipartite synchronization of coupled neural networks with uncertain parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    4. Dong, Tao & Wang, Aijuan & Zhu, Huiyun & Liao, Xiaofeng, 2018. "Event-triggered synchronization for reaction–diffusion complex networks via random sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 454-462.
    5. Xupeng Luo & Haijun Jiang & Jiarong Li & Shanshan Chen & Yang Xia, 2024. "Modeling and controlling delayed rumor propagation with general incidence in heterogeneous networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 35(02), pages 1-29, February.
    6. Stamov, Gani & Stamova, Ivanka & Martynyuk, Anatoliy & Stamov, Trayan, 2021. "Almost periodic dynamics in a new class of impulsive reaction–diffusion neural networks with fractional-like derivatives," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    7. Chen, Shenglong & Yang, Jikai & Li, Zhiming & Li, Hong-Li & Hu, Cheng, 2023. "New results for dynamical analysis of fractional-order gene regulatory networks with time delay and uncertain parameters," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
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