An Intelligent Neural Networks System for Prediction of particleboard properties

AbstracIn the past decade, artificial neural networks have been used as a powerful tool for modeling and prediction in many scientific fields. In this study, the feed-forward multilayer Perceptron (MLP) was utilized and trained by back propagation (BP) algorithm with Levenberg-Marquardt numerical op...

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Autores principales:	Zahra Jahani lomer, Saeed Reza farrokhpayam, Mohammad Shamsian
Formato:	article
Lenguaje:	FA
Publicado:	Regional Information Center for Science and Technology (RICeST) 2014
Materias:	Key words: Modeling Artificial Neural Networkو particleboard physical properties Forestry SD1-669.5
Acceso en línea:	https://doaj.org/article/140c6ee6d950425089ba5ec82cb7a0f2
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id	oai:doaj.org-article:140c6ee6d950425089ba5ec82cb7a0f2
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spelling	oai:doaj.org-article:140c6ee6d950425089ba5ec82cb7a0f22021-12-02T11:55:26ZAn Intelligent Neural Networks System for Prediction of particleboard properties1735-09132383-112X10.22092/ijwpr.2014.5687https://doaj.org/article/140c6ee6d950425089ba5ec82cb7a0f22014-06-01T00:00:00Zhttp://ijwpr.areeo.ac.ir/article_5687_0e24135393ad8578e35ccea67e59f020.pdfhttps://doaj.org/toc/1735-0913https://doaj.org/toc/2383-112XAbstracIn the past decade, artificial neural networks have been used as a powerful tool for modeling and prediction in many scientific fields. In this study, the feed-forward multilayer Perceptron (MLP) was utilized and trained by back propagation (BP) algorithm with Levenberg-Marquardt numerical optimization technique via Matlab software. Temperature of press (°C), mat moisture content (%) and press closing time (sec) were used as inputs, Water absorption (WA2, 24h), thickness swelling (TS2, 24h) and density were the outputs of neural network model. This technique will increase network versatility and decreases the effect of undesirable and weak data. The modeling and prediction was done based experimental data and the forecasting results were compared with real data. The efficiency of these techniques evaluated with statistical criteria of mean square error (MSE), root mean square error, (RMSE) and the correlation coefficient (R2). The results showed this ANN model could accurately describe the water absorption, thickness swelling after immersion for 2 and 24 hours, and density of particleboardZahra Jahani lomerSaeed Reza farrokhpayamMohammad ShamsianRegional Information Center for Science and Technology (RICeST)articleKey words: ModelingArtificial Neural Networkو particleboardphysical propertiesForestrySD1-669.5FAتحقیقات علوم چوب و کاغذ ایران, Vol 29, Iss 2, Pp 242-253 (2014)
institution	DOAJ
collection	DOAJ
language	FA
topic	Key words: Modeling Artificial Neural Networkو particleboard physical properties Forestry SD1-669.5
spellingShingle	Key words: Modeling Artificial Neural Networkو particleboard physical properties Forestry SD1-669.5 Zahra Jahani lomer Saeed Reza farrokhpayam Mohammad Shamsian An Intelligent Neural Networks System for Prediction of particleboard properties
description	AbstracIn the past decade, artificial neural networks have been used as a powerful tool for modeling and prediction in many scientific fields. In this study, the feed-forward multilayer Perceptron (MLP) was utilized and trained by back propagation (BP) algorithm with Levenberg-Marquardt numerical optimization technique via Matlab software. Temperature of press (°C), mat moisture content (%) and press closing time (sec) were used as inputs, Water absorption (WA2, 24h), thickness swelling (TS2, 24h) and density were the outputs of neural network model. This technique will increase network versatility and decreases the effect of undesirable and weak data. The modeling and prediction was done based experimental data and the forecasting results were compared with real data. The efficiency of these techniques evaluated with statistical criteria of mean square error (MSE), root mean square error, (RMSE) and the correlation coefficient (R2). The results showed this ANN model could accurately describe the water absorption, thickness swelling after immersion for 2 and 24 hours, and density of particleboard
format	article
author	Zahra Jahani lomer Saeed Reza farrokhpayam Mohammad Shamsian
author_facet	Zahra Jahani lomer Saeed Reza farrokhpayam Mohammad Shamsian
author_sort	Zahra Jahani lomer
title	An Intelligent Neural Networks System for Prediction of particleboard properties
title_short	An Intelligent Neural Networks System for Prediction of particleboard properties
title_full	An Intelligent Neural Networks System for Prediction of particleboard properties
title_fullStr	An Intelligent Neural Networks System for Prediction of particleboard properties
title_full_unstemmed	An Intelligent Neural Networks System for Prediction of particleboard properties
title_sort	intelligent neural networks system for prediction of particleboard properties
publisher	Regional Information Center for Science and Technology (RICeST)
publishDate	2014
url	https://doaj.org/article/140c6ee6d950425089ba5ec82cb7a0f2
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_version_	1718394791266353152

An Intelligent Neural Networks System for Prediction of particleboard properties

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