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Research Article | Open Access

Finite-time stability for fractional-order fuzzy neural network with mixed delays and inertial terms

Tiecheng Zhang1Liyan Wang2( )Yuan Zhang1Jiangtao Deng1
Huangshi Key Laboratory of Metaverse and Virtual Simulation, School of Mathematics and Statistics, Hubei Normal University, Huangshi, Hubei 435002, China
School of Automation, Hubei University of Science and Technology, Xianning, Hubei 437100, China
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Abstract

This paper explored the finite-time stability (FTS) of fractional-order fuzzy inertial neural network with mixed delays. First, the dimension of the model was reduced by the order reduction method. Second, by leveraging the fractional-order finite-time stability theorem, fractional calculus and inequality methods, we established some sufficient conditions to guarantee the FTS of the model under feasible delay-dependent feedback controller and delay-dependent adaptive controller, respectively. Additionally, we derived the settling times (STs) for each control strategy. Finally, we provided two examples to substantiate our findings.

CLC number: 93D09, 93D20, 93D23

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AIMS Mathematics
Pages 19176-19194

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Cite this article:
Zhang T, Wang L, Zhang Y, et al. Finite-time stability for fractional-order fuzzy neural network with mixed delays and inertial terms. AIMS Mathematics, 2024, 9(7): 19176-19194. https://doi.org/10.3934/math.2024935

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Received: 15 April 2024
Revised: 24 May 2024
Accepted: 04 June 2024
Published: 15 July 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)