Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations

Houssem Jerbi, Hadeel Alharbi, Mohamed Omri, Lotfi Ladhar, Theodore E. Simos (), Spyridon D. Mourtas and Vasilios N. Katsikis
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Houssem Jerbi: Department of Industrial Engineering, College of Engineering, University of Háil, Hail 1234, Saudi Arabia
Hadeel Alharbi: Department of Computer Engineering, College of Computer Science and Engineering, University of Háil, Hail 1234, Saudi Arabia
Mohamed Omri: Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
Lotfi Ladhar: Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdul Aziz University, Jeddah 21589, 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, 2022, vol. 10, issue 23, 1-16

Abstract: One of the most often used approaches for approximating various matrix equation problems is the hyperpower family of iterative methods with arbitrary convergence order, whereas the zeroing neural network (ZNN) is a type of neural dynamics intended for handling time-varying problems. A family of ZNN models that correlate with the hyperpower iterative methods is defined on the basis of the analogy that was discovered. These models, known as higher-order ZNN models (HOZNN), can be used to find real symmetric solutions of time-varying algebraic Riccati equations. Furthermore, a noise-handling HOZNN (NHOZNN) class of dynamical systems is introduced. The traditional ZNN and HOZNN dynamic flows are compared theoretically and numerically.

Keywords: zeroing neural networks; hyperpower iterations; algebraic Riccati equations; 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 (2)

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