Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid

In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means square...

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Autores principales:	Jawaher Lafi Aljohani, Eman Salem Alaidarous, Muhammad Asif Zahoor Raja, Muhammed Shabab Alhothuali, Muhammad Shoaib
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Wire coating Sisko fluid Levenberg Marquardt Intelligent computing Artificial neural network Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/8b832e40eac440a4adf09b8b919efaa6
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id	oai:doaj.org-article:8b832e40eac440a4adf09b8b919efaa6
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spelling	oai:doaj.org-article:8b832e40eac440a4adf09b8b919efaa62021-11-22T04:21:19ZBackpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid2090-447910.1016/j.asej.2021.03.007https://doaj.org/article/8b832e40eac440a4adf09b8b919efaa62021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001581https://doaj.org/toc/2090-4479In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means squared errors. In this paper, the analysis of fluid flow problem based on WCS-SFF is studied with a new application of intelligent computing system via supervised learning mechanism using the efficacy of neural networks trained by Levenberg-Marquardt algorithm (NN-TLMA). The original mathematical formulation in terms of PDEs for WCS-SFF is converted into dimensionless nonlinear ODEs. The data collection for the projected NN-TLMA is produced for parameters associated with the system model WCS-SFF influencing the velocity using the explicit Runge-Kutta technique. The training, validation, and testing processes of NN-TLMA are utilized to evaluate the obtained results of WCS-SFF for various cases, and a comparison of the obtained results is performed with reference data set to check the accuracy and effectiveness of the proposed algorithm NN-TLMA for the analysis of non-Newtonian fluid problem-related WCS-SFF. The proposed NN-TLMA for solving the WCS-SFF is effectively confirmed through state transition dynamics, mean square error, regression analyses, and error histogram studies. The powerful consistency of suggested outcomes with reference solutions indicates the validity of the framework, and the accuracy of 10-8 to 10-6 is also achieved.Jawaher Lafi AljohaniEman Salem AlaidarousMuhammad Asif Zahoor RajaMuhammed Shabab AlhothualiMuhammad ShoaibElsevierarticleWire coatingSisko fluidLevenberg MarquardtIntelligent computingArtificial neural networkEngineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 4133-4143 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wire coating Sisko fluid Levenberg Marquardt Intelligent computing Artificial neural network Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Wire coating Sisko fluid Levenberg Marquardt Intelligent computing Artificial neural network Engineering (General). Civil engineering (General) TA1-2040 Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammed Shabab Alhothuali Muhammad Shoaib Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
description	In the artificial neural networks domain, the Levenberg-Marquardt technique is novel with convergent stability and generates a numerical solution of the wire coating system for Sisko fluid flow (WCS-SFF) through regression plots, histogram representations, state transition measures, and means squared errors. In this paper, the analysis of fluid flow problem based on WCS-SFF is studied with a new application of intelligent computing system via supervised learning mechanism using the efficacy of neural networks trained by Levenberg-Marquardt algorithm (NN-TLMA). The original mathematical formulation in terms of PDEs for WCS-SFF is converted into dimensionless nonlinear ODEs. The data collection for the projected NN-TLMA is produced for parameters associated with the system model WCS-SFF influencing the velocity using the explicit Runge-Kutta technique. The training, validation, and testing processes of NN-TLMA are utilized to evaluate the obtained results of WCS-SFF for various cases, and a comparison of the obtained results is performed with reference data set to check the accuracy and effectiveness of the proposed algorithm NN-TLMA for the analysis of non-Newtonian fluid problem-related WCS-SFF. The proposed NN-TLMA for solving the WCS-SFF is effectively confirmed through state transition dynamics, mean square error, regression analyses, and error histogram studies. The powerful consistency of suggested outcomes with reference solutions indicates the validity of the framework, and the accuracy of 10-8 to 10-6 is also achieved.
format	article
author	Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammed Shabab Alhothuali Muhammad Shoaib
author_facet	Jawaher Lafi Aljohani Eman Salem Alaidarous Muhammad Asif Zahoor Raja Muhammed Shabab Alhothuali Muhammad Shoaib
author_sort	Jawaher Lafi Aljohani
title	Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
title_short	Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
title_full	Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
title_fullStr	Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
title_full_unstemmed	Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid
title_sort	backpropagation of levenberg marquardt artificial neural networks for wire coating analysis in the bath of sisko fluid
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8b832e40eac440a4adf09b8b919efaa6
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Backpropagation of Levenberg Marquardt artificial neural networks for wire coating analysis in the bath of Sisko fluid

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