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Research Article | Open Access

Synchronization robustness analysis of memristive-based neural networks with deviating arguments and stochastic perturbations

Tao Xie( ), Xing Xiong, Qike Zhang
School of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002, China
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Abstract

In this article, we investigate the robustness of memristive-based neural networks (MNNs) with deviating arguments (DAs) and stochastic perturbations (SPs). Based on the set-valued mapping method, differential inclusion theory and Gronwall inequalities, we derive the upper bounds for the width of DAs and the intensity of SPs. When the DAs and SPs are smaller than these upper bounds, the MNNs maintains exponential synchronization. Finally, several specific simulation examples demonstrate the effectiveness of the results.

Keywords

memristive-based neural networksdeviating argumentsstochastic perturbationsexponential synchronization
CLC number: 93B35, 93D23

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AIMS Mathematics
Volume 9 Issue 1,
January 2024
Pages 918-941
DOI: 10.3934/math.2024046

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Cite this article:
Xie T, Xiong X, Zhang Q. Synchronization robustness analysis of memristive-based neural networks with deviating arguments and stochastic perturbations. AIMS Mathematics, 2024, 9(1): 918-941. https://doi.org/10.3934/math.2024046

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Received: 10 October 2023
Revised: 14 November 2023
Accepted: 23 November 2023
Published: 15 January 2024
©2024 the Author(s), licensee AIMS Press.

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