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Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control


  • Received: 13 September 2022 Revised: 17 November 2022 Accepted: 22 November 2022 Published: 07 April 2023
  • This article focuses on achieving fixed-time synchronization (FxTS) of nonlinear coupled memristive neural networks (NCMMN) with time delays. We propose a novel integrable sliding-mode manifold (SMM) and develop two control strategies (chattering or non-chattering) to achieve FxTS. By selecting appropriate parameters, some criteria are established to force the dynamics of NCMMN to reach the designed SMM within a fixed time and remain on it thereafter. Additionally, they provide estimations for the settling time (TST). the validity of our results is demonstrated through several numerical examples.

    Citation: Xingting Geng, Jianwen Feng, Yi Zhao, Na Li, Jingyi Wang. Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control[J]. Electronic Research Archive, 2023, 31(6): 3291-3308. doi: 10.3934/era.2023166

    Related Papers:

  • This article focuses on achieving fixed-time synchronization (FxTS) of nonlinear coupled memristive neural networks (NCMMN) with time delays. We propose a novel integrable sliding-mode manifold (SMM) and develop two control strategies (chattering or non-chattering) to achieve FxTS. By selecting appropriate parameters, some criteria are established to force the dynamics of NCMMN to reach the designed SMM within a fixed time and remain on it thereafter. Additionally, they provide estimations for the settling time (TST). the validity of our results is demonstrated through several numerical examples.



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