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Computation of Time-Varying {2,3}- and {2,4}-Inverses through Zeroing Neural Networks

Xingyuan Li, Chia-Liang Lin, Theodore E. Simos (), Spyridon D. Mourtas and Vasilios N. Katsikis
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Xingyuan Li: Zhejiang Fashion Institute of Technology, 495 Fenghua Road, Ningbo 315211, China
Chia-Liang Lin: General Department, National & Kapodistrian University of Athens, Euripus Campus, 34400 Evia, Greece
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, 2022, vol. 10, issue 24, 1-13

Abstract: This paper investigates the problem of computing the time-varying {2,3}- and {2,4}-inverses through the zeroing neural network (ZNN) method, which is presently regarded as a state-of-the-art method for computing the time-varying matrix Moore–Penrose inverse. As a result, two new ZNN models, dubbed ZNN23I and ZNN24I, for the computation of the time-varying {2,3}- and {2,4}-inverses, respectively, are introduced, and the effectiveness of these models is evaluated. Numerical experiments investigate and confirm the efficiency of the proposed ZNN models for computing the time-varying {2,3}- and {2,4}-inverses.

Keywords: {2,3}-inverse; {2,4}-inverse; zeroing neural networks; dynamical system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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