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Computational Performances Of Morlet Wavelet Neural Network For Solving A Nonlinear Dynamic Based On The Mathematical Model Of The Affection Of Layla And Majnun

Author

Listed:
  • ZULQURNAIN SABIR

    (Department of Mathematics, Near East University, Nicosia 99138, Cyprus†Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon)

  • DUMITRU BALEANU

    (��Department of Mathematics, Cankaya University, Ankara, Turkey§Institute of Space Sciences, Magurele, Romania¶Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan∥Near East University, Mathematics Research Center, Nicosia, 99138, North Cyprus, Mersin 10, Turkey)

  • MUHAMMAD ASIF ZAHOOR RAJA

    (*Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan, R.O.C.)

  • ALI S. ALSHOMRANI

    (��†Faculty of Science, Department Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • EVREN HINCAL

    (Department of Mathematics, Near East University, Nicosia 99138, Cyprus)

Abstract

The aim of this study is to design a novel stochastic solver through the Morlet wavelet neural networks (MWNNs) for solving the mathematical Layla and Majnun (LM) system. The numerical representations of the mathematical LM system have been presented by using the MWNNs along with the optimization is performed through the hybridization of the global and local search schemes. The local active-set (AS) and global genetic algorithm (GA) operators have been used to optimize an error-based merit function using the differential LM model and its initial conditions. The correctness of the MWNNs using the local and global operators is observed through the comparison of the obtained solutions and the Adams scheme, which is used as a reference solution. For the stability of the MWNNs using the global and local operators, the statistical performances with different operators have been provided using the multiple executions to solve the nonlinear LM system.

Suggested Citation

  • Zulqurnain Sabir & Dumitru Baleanu & Muhammad Asif Zahoor Raja & Ali S. Alshomrani & Evren Hincal, 2023. "Computational Performances Of Morlet Wavelet Neural Network For Solving A Nonlinear Dynamic Based On The Mathematical Model Of The Affection Of Layla And Majnun," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-18.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:02:n:s0218348x23400169
    DOI: 10.1142/S0218348X23400169
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    Cited by:

    1. Zulqurnain Sabir & Atef F. Hashem & Adnène Arbi & Mohamed A. Abdelkawy, 2023. "Designing a Bayesian Regularization Approach to Solve the Fractional Layla and Majnun System," Mathematics, MDPI, vol. 11(17), pages 1-13, September.

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