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Research Article | Open Access

Anti-periodic synchronization of quaternion-valued high-order Hopfield neural networks with delays

Jin Gao1( )Lihua Dai2
School of Information, Yunnan Communications Vocational and Technical College, Kunming, Yunnan 650500, China
School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
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Abstract

This paper proposes a class of quaternion-valued high-order Hopfield neural networks with delays. By using the non-decomposition method, non-reduced order method, analytical techniques in uniform convergence functions sequence, and constructing Lyapunov function, we obtain several sufficient conditions for the existence and global exponential synchronization of anti-periodic solutions for delayed quaternion-valued high-order Hopfield neural networks. Finally, an example and its numerical simulations are given to support the proposed approach. Our results play an important role in designing inertial neural networks.

CLC number: 34D06, 34D23, 34K13, 34K24

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AIMS Mathematics
Pages 14051-14075

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Cite this article:
Gao J, Dai L. Anti-periodic synchronization of quaternion-valued high-order Hopfield neural networks with delays. AIMS Mathematics, 2022, 7(8): 14051-14075. https://doi.org/10.3934/math.2022775

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Received: 24 March 2022
Revised: 05 May 2022
Accepted: 20 May 2022
Published: 15 August 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)