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Global Synchronization of Fractional-Order Multi-Delay Coupled Neural Networks with Multi-Link Complicated Structures via Hybrid Impulsive Control

Author

Listed:
  • Hongguang Fan

    (College of Computer, Chengdu University, Chengdu 610106, China
    Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian 351100, China
    School of Mathematical and Computational Science, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Yue Rao

    (College of Computer, Chengdu University, Chengdu 610106, China)

  • Kaibo Shi

    (School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China)

  • Hui Wen

    (Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian 351100, China
    New Engineering Industry College, Putian University, Putian 351100, China)

Abstract

This study discusses the global asymptotical synchronization of fractional-order multi-delay coupled neural networks (FMCNNs) via hybrid control schemes. In addition to internal delays and different coupling delays, more importantly, multi-link complicated structures are introduced into our model. Unlike most existing works, the synchronization target is not the special solution of an isolated node, and a more universally accepted synchronization goal involving the average neuron states is introduced. A generalized multi-delay impulsive comparison principle with fractional order is given to solve the difficulties resulting from different delays and multi-link structures. To reduce control costs, a pinned node strategy based on the principle of statistical sorting is provided, and then a new hybrid impulsive pinning control method is established. Based on fractional-order impulsive inequalities, Laplace transforms, and fractional order stability theory, novel synchronization criteria are derived to guarantee the asymptotical synchronization of the considered FMCNN. The derived theoretical results can effectively extend the existing achievements for fractional-order neural networks with a multi-link nature.

Suggested Citation

  • Hongguang Fan & Yue Rao & Kaibo Shi & Hui Wen, 2023. "Global Synchronization of Fractional-Order Multi-Delay Coupled Neural Networks with Multi-Link Complicated Structures via Hybrid Impulsive Control," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3051-:d:1190808
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    References listed on IDEAS

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