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Research Article | Open Access

Robust synchronization analysis of delayed fractional order neural networks with uncertain parameters

Xinxin Zhang( ), Yunpeng Ma, Shan Gao, Jiancai Song, Lei Chen
School of Information Engineering, Tianjin University of Commerce, Guangrongdao Road 409, Tianjin, China
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Abstract

This paper is concerned with the robust synchronization analysis of delayed fractional order neural networks with uncertain parameters (DFNNUPs). Firstly, the DFNNUPs drive system model and response system model are established. Secondly, using multiple matrix quadratic Lyapunov function approach and inequality analysis technique, the robust synchronization conditions are derived in the form of the matrix inequalities. Finally, the correctness of the theoretical results is verified by an example.

Keywords

robust synchronizationdelayed fractional order neural networksuncertain parametersmultiple matrix quadratic Lyapunov functionmatrix inequalities
CLC number: 93A30, 93D09

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AIMS Mathematics
Volume 7 Issue 10,
October 2022
Pages 18883-18896
DOI: 10.3934/math.20221040

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Cite this article:
Zhang X, Ma Y, Gao S, et al. Robust synchronization analysis of delayed fractional order neural networks with uncertain parameters. AIMS Mathematics, 2022, 7(10): 18883-18896. https://doi.org/10.3934/math.20221040

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Received: 15 June 2022
Revised: 27 July 2022
Accepted: 12 August 2022
Published: 15 October 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)