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Research Article | Open Access

Accuracy-preassigned fixed-time synchronization of inertial neural networks with time-varying leakage delays and proportional delays

Er-Yong Cong1,2( )Xian Zhang3,4( )Li Zhu1
Department of Mathematics, Harbin University, Harbin 150086, China
Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software, Harbin University, Harbin 150080, China
School of Mathematical Science, Heilongjiang University, Harbin 150080, China
Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Heilongjiang University, Harbin 150080, China
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Abstract

This work established synchronization criteria for master-slave inertial neural networks with leakage time-varying delays and proportional delays, The solution employed a direct analysis method based on parameterized system solutions. The derived synchronization conditions consisted of only a few simple inequalities, which were easy to solve.Based on the adopted approach, a novel class of synchronization controllers was designed for proportional delays without constructing any complex functionals. Using the proposed method, leakage delays could be transformed into their maximum absolute values, enabling the derivation of delay-dependent conditions without any intricate treatment of leakage delays. Furthermore, it was noteworthy that this paper presented the first investigation into this problem using the proposed method, and the technique employed was novel. Finally, numerical simulations were provided to verify the effectiveness of the proposed method.

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Electronic Research Archive
Pages 5897-5915

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Cite this article:
Cong E-Y, Zhang X, Zhu L. Accuracy-preassigned fixed-time synchronization of inertial neural networks with time-varying leakage delays and proportional delays. Electronic Research Archive, 2025, 33(10): 5897-5915. https://doi.org/10.3934/era.2025262

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Received: 26 July 2025
Revised: 19 September 2025
Accepted: 26 September 2025
Published: 09 October 2025
©2025 the Author(s), licensee AIMS Press.

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