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Research Article | Open Access

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

Xingting Geng1Jianwen Feng1Yi Zhao1Na Li2Jingyi Wang1( )
College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, PR China
College of Mathematics and Statistics, Henan University, Kaifeng 475004, PR China
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Abstract

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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Electronic Research Archive
Pages 3291-3308

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Cite this article:
Geng X, Feng J, Zhao Y, et al. Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control. Electronic Research Archive, 2023, 31(6): 3291-3308. https://doi.org/10.3934/era.2023166

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Received: 13 September 2022
Revised: 17 November 2022
Accepted: 22 November 2022
Published: 15 June 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)