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Research Article | Open Access

New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects

Yinjie Qian, Lian Duan( ), Hui Wei
School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232001, China
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Abstract

In this paper, we further investigate the finite-/fixed-time synchronization (FFTS) problem for a class of delayed memristive reaction-diffusion neural networks (MRDNNs). By utilizing the state-feedback control techniques, and constructing a general Lyapunov functional, with the help of inequality techniques and the finite-time stability theory, novel criteria are established to realize the FFTS of the considered delayed MRDNNs, which generalize and complement previously known results. Finally, a numerical example is provided to support the obtained theoretical results.

Keywords

memristive neural networkfinite-/fixed-time synchronizationdiffusion effect
CLC number: 34K39, 93D05

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AIMS Mathematics
Volume 7 Issue 9,
September 2022
Pages 16962-16974
DOI: 10.3934/math.2022931

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Cite this article:
Qian Y, Duan L, Wei H. New results on finite-/fixed-time synchronization of delayed memristive neural networks with diffusion effects. AIMS Mathematics, 2022, 7(9): 16962-16974. https://doi.org/10.3934/math.2022931

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Received: 07 February 2022
Revised: 24 June 2022
Accepted: 03 July 2022
Published: 15 September 2022
©2022 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)