A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm

Abstract In this study, GMDH neural network based on genetic algorithm was used to predict the physical and mechanical properties of laboratory made particleboard. To predict the mechanical and physical properties of particleboard we used input parameters such as neural network including press clos...

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Autores principales:	Zahra Jahanilomer, Saeed Reza farrokhpayam, Mohammad Shamsian
Formato:	article
Lenguaje:	FA
Publicado:	Regional Information Center for Science and Technology (RICeST) 2014
Materias:	Keywords: Particleboard modeling GMDH-Type Neural Netwok Physical Mechanical Properties Forestry SD1-669.5
Acceso en línea:	https://doaj.org/article/eeb2d32a64cc4842afc09778291d3542
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spelling	oai:doaj.org-article:eeb2d32a64cc4842afc09778291d35422021-12-02T03:52:28ZA mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm1735-09132383-112X10.22092/ijwpr.2014.6130https://doaj.org/article/eeb2d32a64cc4842afc09778291d35422014-09-01T00:00:00Zhttp://ijwpr.areeo.ac.ir/article_6130_301e53ab2aa4650e82fcb5c248734664.pdfhttps://doaj.org/toc/1735-0913https://doaj.org/toc/2383-112XAbstract In this study, GMDH neural network based on genetic algorithm was used to predict the physical and mechanical properties of laboratory made particleboard. To predict the mechanical and physical properties of particleboard we used input parameters such as neural network including press closing time (10,20 and 30 seconds), moisture content of the mat (8,10,12 and 14%) and press temperature (150,160,170 and 180°C) as the input data and the output data was the physical and mechanical properties. The efficiency of these techniques was evaluated with statistical criteria of mean square error (MSE), root mean square error, (RMSE), mean absolute deviation (MAD) and the correlation coefficient (R2). Results showed that the value of MSE, RMSE and MAD for MOR, IB, TS24h, TS2h, WA2h and WA24h is low. Errors obtained for the MOE model were very high. According to the results obtained, this model is not the appropriate for prediction of MOE. R2 values from the test and training set properties for MOR, IB, MOE, TS24h, TS2h, WA2h and WA24hwas more than 0.91%, which reflects that the performance of these models is better.Zahra JahanilomerSaeed Reza farrokhpayamMohammad ShamsianRegional Information Center for Science and Technology (RICeST)articleKeywords: ParticleboardmodelingGMDH-Type Neural NetwokPhysical Mechanical PropertiesForestrySD1-669.5FAتحقیقات علوم چوب و کاغذ ایران, Vol 29, Iss 3, Pp 376-389 (2014)
institution	DOAJ
collection	DOAJ
language	FA
topic	Keywords: Particleboard modeling GMDH-Type Neural Netwok Physical Mechanical Properties Forestry SD1-669.5
spellingShingle	Keywords: Particleboard modeling GMDH-Type Neural Netwok Physical Mechanical Properties Forestry SD1-669.5 Zahra Jahanilomer Saeed Reza farrokhpayam Mohammad Shamsian A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
description	Abstract In this study, GMDH neural network based on genetic algorithm was used to predict the physical and mechanical properties of laboratory made particleboard. To predict the mechanical and physical properties of particleboard we used input parameters such as neural network including press closing time (10,20 and 30 seconds), moisture content of the mat (8,10,12 and 14%) and press temperature (150,160,170 and 180°C) as the input data and the output data was the physical and mechanical properties. The efficiency of these techniques was evaluated with statistical criteria of mean square error (MSE), root mean square error, (RMSE), mean absolute deviation (MAD) and the correlation coefficient (R2). Results showed that the value of MSE, RMSE and MAD for MOR, IB, TS24h, TS2h, WA2h and WA24h is low. Errors obtained for the MOE model were very high. According to the results obtained, this model is not the appropriate for prediction of MOE. R2 values from the test and training set properties for MOR, IB, MOE, TS24h, TS2h, WA2h and WA24hwas more than 0.91%, which reflects that the performance of these models is better.
format	article
author	Zahra Jahanilomer Saeed Reza farrokhpayam Mohammad Shamsian
author_facet	Zahra Jahanilomer Saeed Reza farrokhpayam Mohammad Shamsian
author_sort	Zahra Jahanilomer
title	A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
title_short	A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
title_full	A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
title_fullStr	A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
title_full_unstemmed	A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm
title_sort	mathematical model to predict particleboard properties using the gmdh-type neural network and genetic algorithm
publisher	Regional Information Center for Science and Technology (RICeST)
publishDate	2014
url	https://doaj.org/article/eeb2d32a64cc4842afc09778291d3542
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A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm

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