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Levenberg–Marquardt Backpropagation for Numerical Treatment of Micropolar Flow in a Porous Channel with Mass Injection

Author

Listed:
  • Hakeem Ullah
  • Imran Khan
  • Hussain AlSalman
  • Saeed Islam
  • Muhammad Asif Zahoor Raja
  • Muhammad Shoaib
  • Abdu Gumaei
  • Mehreen Fiza
  • Kashif Ullah
  • Sk. Md. Mizanur Rahman
  • Muhammad Ayaz
  • Murari Andrea

Abstract

In this research work, an effective Levenberg–Marquardt algorithm-based artificial neural network (LMA-BANN) model is presented to find an accurate series solution for micropolar flow in a porous channel with mass injection (MPFPCMI). The LMA is one of the fastest backpropagation methods used for solving least-squares of nonlinear problems. We create a dataset to train, test, and validate the LMA-BANN model regarding the solution obtained by optimal homotopy asymptotic (OHA) method. The proposed model is evaluated by conducting experiments on a dataset acquired from the OHA method. The experimental results are obtained by using mean square error (MSE) and absolute error (AE) metric functions. The learning process of the adjustable parameters is conducted with efficacy of the LMA-BANN model. The performance of the developed LMA-BANN for the modelled problem is confirmed by achieving the best promise numerical results of performance in the range of E-05 to E-08 and also assessed by error histogram plot (EHP) and regression plot (RP) measures.

Suggested Citation

  • Hakeem Ullah & Imran Khan & Hussain AlSalman & Saeed Islam & Muhammad Asif Zahoor Raja & Muhammad Shoaib & Abdu Gumaei & Mehreen Fiza & Kashif Ullah & Sk. Md. Mizanur Rahman & Muhammad Ayaz & Murari A, 2021. "Levenberg–Marquardt Backpropagation for Numerical Treatment of Micropolar Flow in a Porous Channel with Mass Injection," Complexity, Hindawi, vol. 2021, pages 1-12, December.
    • Handle: RePEc:hin:complx:5337589
    DOI: 10.1155/2021/5337589
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    Cited by:

    1. Mohamed Elgharib Gomah & Guichen Li & Naseer Muhammad Khan & Changlun Sun & Jiahui Xu & Ahmed A. Omar & B. G. Mousa & Marzouk Mohamed Aly Abdelhamid & M. M. Zaki, 2022. "Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    2. Muhammad Ali & Naseer Muhammad Khan & Qiangqiang Gao & Kewang Cao & Danial Jahed Armaghani & Saad S. Alarifi & Hafeezur Rehman & Izhar Mithal Jiskani, 2023. "Prediction of Coal Dilatancy Point Using Acoustic Emission Characteristics: Insight Experimental and Artificial Intelligence Approaches," Mathematics, MDPI, vol. 11(6), pages 1-25, March.

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