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Finite-time bipartite synchronization of coupled neural networks with uncertain parameters

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  • Mao, Kun
  • Liu, Xiaoyang
  • Cao, Jinde
  • Hu, Yuanfa

Abstract

This paper investigates the finite-time bipartite synchronization (FTBS) of coupled neural networks (CNNs) with uncertain parameters. Under signed graphs, two control strategies are designed to guarantee FTBS of CNNs with or without external disturbances, respectively. For the disturbed CNNs, a discontinuous adaptive control strategy is proposed to overcome the impacts caused by uncertain parameters and disturbances with the help of non-smooth analysis and Lyapunov stability theory. For the undisturbed CNNs, a periodically intermittent adaptive control strategy is developed to achieve FTBS. Finally, two numerical examples are provided to demonstrate the effectiveness of theoretical results.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:585:y:2022:i:c:s0378437121007044
    DOI: 10.1016/j.physa.2021.126431
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    References listed on IDEAS

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    Cited by:

    1. Hongguang Fan & Jiahui Tang & Kaibo Shi & Yi Zhao & Hui Wen, 2023. "Delayed Impulsive Control for μ -Synchronization of Nonlinear Multi-Weighted Complex Networks with Uncertain Parameter Perturbation and Unbounded Delays," Mathematics, MDPI, vol. 11(1), pages 1-17, January.
    2. Fan, Hongguang & Shi, Kaibo & Zhao, Yi, 2022. "Global μ-synchronization for nonlinear complex networks with unbounded multiple time delays and uncertainties via impulsive control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).

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