Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks

Hadeel Alharbi, Houssem Jerbi, Mourad Kchaou, Rabeh Abbassi, Theodore E. Simos (), Spyridon D. Mourtas and Vasilios N. Katsikis
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Hadeel Alharbi: Department of Computer Science, College of Computer Science and Engineering, University of Hail, Hail 1234, Saudi Arabia
Houssem Jerbi: Department of Industrial Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Mourad Kchaou: Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Rabeh Abbassi: Department of Electrical Engineering, College of Engineering, University of Hail, Hail 1234, Saudi Arabia
Theodore E. Simos: Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
Spyridon D. Mourtas: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Vasilios N. Katsikis: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece

Mathematics, 2023, vol. 11, issue 3, 1-14

Abstract: The computation of the time-varying matrix pseudoinverse has become crucial in recent years for solving time-varying problems in engineering and science domains. This paper investigates the issue of calculating the time-varying pseudoinverse based on full-rank decomposition (FRD) using the zeroing neural network (ZNN) method, which is currently considered to be a cutting edge method for calculating the time-varying matrix pseudoinverse. As a consequence, for the first time in the literature, a new ZNN model called ZNNFRDP is introduced for time-varying pseudoinversion and it is based on FRD. Five numerical experiments investigate and confirm that the ZNNFRDP model performs as well as, if not better than, other well-performing ZNN models in the calculation of the time-varying pseudoinverse. Additionally, theoretical analysis and numerical findings have both supported the effectiveness of the proposed model.

Keywords: pseudoinversion; dynamical system; full-rank decomposition; zeroing neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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