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Research Article | Open Access

Zeroing neural network model for solving a generalized linear time-varying matrix equation

Huamin Zhang1( ), Hongcai Yin2
College of Information and Network Engineering, Anhui Science and Technology University, Bengbu 233030, China
School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233000, China
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Abstract

The time-varying solution of a class generalized linear matrix equation with the transpose of an unknown matrix is discussed. The computation model is constructed and asymptotic convergence proof is given by using the zeroing neural network method. Using an activation function, the predefined-time convergence property and noise suppression strategy are discussed. Numerical examples are offered to illustrate the efficacy of the suggested zeroing neural network models.

Keywords

linear time-varying matrix equationzeroing neural networkconvergence analysis
CLC number: 15A09, 15A24

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AIMS Mathematics
Volume 7 Issue 2,
February 2022
Pages 2266-2280
DOI: 10.3934/math.2022129

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Cite this article:
Zhang H, Yin H. Zeroing neural network model for solving a generalized linear time-varying matrix equation. AIMS Mathematics, 2022, 7(2): 2266-2280. https://doi.org/10.3934/math.2022129

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Received: 11 August 2021
Accepted: 29 October 2021
Published: 15 February 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)