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

Author

Listed:
  • 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
    Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
    Data Recovery Key Laboratory of Sichun Province, Neijing Normal University, Neijiang 641100, China
    Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Spyridon D. Mourtas

    (Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
    Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia)

  • Vasilios N. Katsikis

    (Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece)

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.

Suggested Citation

  • Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:600-:d:1045659
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    References listed on IDEAS

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    1. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
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    3. Spyridon D. Mourtas, 2022. "A weights direct determination neuronet for time‐series with applications in the industrial indices of the Federal Reserve Bank of St. Louis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1512-1524, November.
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    Cited by:

    1. Jianrong Chen & Xiangui Kang & Yunong Zhang, 2023. "Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices," Mathematics, MDPI, vol. 11(15), pages 1-18, July.
    2. Houssem Jerbi & Obaid Alshammari & Sondess Ben Aoun & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control," Mathematics, MDPI, vol. 12(1), pages 1-19, December.

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