Статья

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

Hakeem UllahDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, Pakistanİmran KhanDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, PakistanHussain AlSalmanDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaSaeed IslamDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, PakistanMuhammad Asif Zahoor RajaFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002Muhammad ShoaibDepartment of Mathematics, COMSATS University Islamabad, Attock Campus, Attock 43600, PakistanAbdu GumaeiDepartment of Computer Science Faculty of Applied Sciences, Taiz University, Taiz 6803, YemenMehreen FizaDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, PakistanKashif UllahDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, PakistanSk. Md. Mizanur RahmanInformation and Communication Engineering Technology, School of Engineering Technology and Applied Science, Centennial College, Toronto, CanadaMuhammad AyazDepartment of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP 23200, Pakistan

2021en

ABI

Аннотация

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.

Перевод пока недоступен

Идентификаторы

DOI: 10.1155/2021/5337589

Цитирования и источники

Цитирований: 3Использованных источников: 0

Показатели — AkademScholar