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Research Article | Open Access

Stability analysis of fractional-order quaternion-valued neural networks with multiple delays and parameter uncertainties

Guoqing Jiang1, Xiaolan Liu1,2,3( ), Lei Wang1, Chongwei Zheng4
College of Mathematics and Statistics, Sichuan University of Science and Engineering, Zigong, Sichuan, 643000, China
Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong, Sichuan, 643000, China
South Sichuan Center for Applied Mathematics, Zigong, Sichuan, 643000, China
Department of Navigation, Dalian Naval Academy, Chinese People's Liberation Army, Dalian, 116000, China
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Abstract

This paper proposes a fractional-order quaternion-valued neural network (FOQVNN) model with dual proportional delays, neutral delays, and parameter uncertainties. By leveraging classical lemmas and a relaxed linear matrix inequality (LMI) condition, we prove not only the uniqueness of the equilibrium point in the proposed model, but also the global robust stability of this equilibrium. Finally, numerical simulations were provided to validate the theoretical results.

Keywords

double proportional delayneutral delayparameter uncertaintyglobally robust stableLMI
CLC number: 34A34, 34D23, 37N25

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AIMS Mathematics
Volume 10 Issue 5,
May 2025
Pages 12205-12227
DOI: 10.3934/math.2025553

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Cite this article:
Jiang G, Liu X, Wang L, et al. Stability analysis of fractional-order quaternion-valued neural networks with multiple delays and parameter uncertainties. AIMS Mathematics, 2025, 10(5): 12205-12227. https://doi.org/10.3934/math.2025553

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Received: 01 April 2025
Revised: 10 May 2025
Accepted: 19 May 2025
Published: 15 May 2025
©2025 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)