TY - JOUR
T1 - Levenberg-Marquardt Backpropagation for Numerical Treatment of Micropolar Flow in a Porous Channel with Mass Injection
AU - Ullah, Hakeem
AU - Khan, Imran
AU - Alsalman, Hussain
AU - Islam, Saeed
AU - Asif Zahoor Raja, Muhammad
AU - Shoaib, Muhammad
AU - Gumaei, Abdu
AU - Fiza, Mehreen
AU - Ullah, Kashif
AU - Mizanur Rahman, Sk Md
AU - Ayaz, Muhammad
N1 - Publisher Copyright:
© 2021 Hakeem Ullah et al.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122336887&partnerID=8YFLogxK
U2 - 10.1155/2021/5337589
DO - 10.1155/2021/5337589
M3 - Article
AN - SCOPUS:85122336887
SN - 1076-2787
VL - 2021
JO - Complexity
JF - Complexity
M1 - 5337589
ER -