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Zeroing neural network approaches for computing time-varying minimal rank outer inverse

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  • Stanimirović, Predrag S.
  • Mourtas, Spyridon D.
  • Mosić, Dijana
  • Katsikis, Vasilios N.
  • Cao, Xinwei
  • Li, Shuai

Abstract

Generalized inverses are extremely effective in many areas of mathematics and engineering. The zeroing neural network (ZNN) technique, which is currently recognized as the state-of-the-art approach for calculating the time-varying Moore-Penrose matrix inverse, is investigated in this study as a solution to the problem of calculating the time-varying minimum rank outer inverse (TV-MROI) with prescribed range and/or TV-MROI with prescribed kernel. As a result, four novel ZNN models are introduced for computing the TV-MROI, and their efficiency is examined. Numerical tests examine and validate the effectiveness of the introduced ZNN models for calculating TV-MROI with prescribed range and/or prescribed kernel.

Suggested Citation

  • Stanimirović, Predrag S. & Mourtas, Spyridon D. & Mosić, Dijana & Katsikis, Vasilios N. & Cao, Xinwei & Li, Shuai, 2024. "Zeroing neural network approaches for computing time-varying minimal rank outer inverse," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    • Handle: RePEc:eee:apmaco:v:465:y:2024:i:c:s0096300323005817
    DOI: 10.1016/j.amc.2023.128412
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    References listed on IDEAS

    as
    1. Lixiong Li, 2018. "A General Method for Demand Inversion," Papers 1802.04444, arXiv.org, revised Feb 2018.
    2. 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.
    3. Zhou, Mengmeng & Chen, Jianlong, 2018. "Integral representations of two generalized core inverses," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 187-193.
    4. Houssem Jerbi & Hadeel Alharbi & Mohamed Omri & Lotfi Ladhar & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2022. "Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
    5. Spyridon D. Mourtas & Chrysostomos Kasimis, 2022. "Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-20, August.
    6. Wendong Jiang & Chia-Liang Lin & Vasilios N. Katsikis & Spyridon D. Mourtas & Predrag S. Stanimirović & Theodore E. Simos, 2022. "Zeroing Neural Network Approaches Based on Direct and Indirect Methods for Solving the Yang–Baxter-like Matrix Equation," Mathematics, MDPI, vol. 10(11), pages 1-13, June.
    7. Dijana Mosić & Predrag S. Stanimirović & Spyridon D. Mourtas, 2023. "Minimal Rank Properties of Outer Inverses with Prescribed Range and Null Space," Mathematics, MDPI, vol. 11(7), pages 1-18, April.
    8. 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.
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