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Event-Based Impulsive Control for Heterogeneous Neural Networks with Communication Delays

Author

Listed:
  • Yilin Li

    (Industrial Training Center, Shenzhen Polytechnic, Shenzhen 518055, China
    College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China)

  • Chengbo Yi

    (Industrial Training Center, Shenzhen Polytechnic, Shenzhen 518055, China)

  • Jianwen Feng

    (College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China)

  • Jingyi Wang

    (College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China)

Abstract

The quasi-synchronization for a class of general heterogeneous neural networks is explored by event-based impulsive control strategy. Compared with the traditional average impulsive interval (AII) method, instead, an event-triggered mechanism (ETM) is employed to determine the impulsive instants, in which case the subjectivity of selecting the controlling sequence can be eliminated. In addition, considering the fact that communication delay is inevitable between the allocation and execution of instructions in practice, we further nominate an ETM centered on communication delays and aperiodic sampling, which is more accessible and affordable, yet can straightforwardly avoid Zeno behavior. Hence, on the basis of the novel event-triggered impulsive control strategy, quasi-synchronization of heterogeneous neural network model is investigated and some general conditions are also achieved. Finally, two numerical simulations are afforded to validate the efficacy of theoretical results.

Suggested Citation

  • Yilin Li & Chengbo Yi & Jianwen Feng & Jingyi Wang, 2022. "Event-Based Impulsive Control for Heterogeneous Neural Networks with Communication Delays," Mathematics, MDPI, vol. 10(24), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4836-:d:1008470
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    References listed on IDEAS

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    1. Jorge Grasa & Begoña Calvo, 2021. "Simulating Extraocular Muscle Dynamics. A Comparison between Dynamic Implicit and Explicit Finite Element Methods," Mathematics, MDPI, vol. 9(9), pages 1-17, May.
    2. Shuo Meng & Ruru Pan & Weidong Gao & Jian Zhou & Jingan Wang & Wentao He, 2021. "A multi-task and multi-scale convolutional neural network for automatic recognition of woven fabric pattern," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1147-1161, April.
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