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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Regional Information Center for Science and Technology (RICeST)
2014-09-01
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doaj-eeb2d32a64cc4842afc09778291d35422020-11-25T00:35:38ZfasRegional Information Center for Science and Technology (RICeST) تحقیقات علوم چوب و کاغذ ایران1735-09132383-112X2014-09-0129337638910.22092/ijwpr.2014.61306130A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithmZahra Jahanilomer0Saeed Reza farrokhpayam1Mohammad Shamsian2M.Sc., Department of Wood and Paper Science &Technology, Faculty of Natural Resources, University of Zabol, Iran,Assistant Professor, Department of Wood and Paper science &Technology, Faculty of Natural Resources, University of Zabol, IranAssistant Professor, Department of Wood and Paper science &Technology, Faculty of Natural Resources, University of Zabol, IranAbstract 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.http://ijwpr.areeo.ac.ir/article_6130_301e53ab2aa4650e82fcb5c248734664.pdfKeywords: ParticleboardmodelingGMDH-Type Neural NetwokPhysical Mechanical Properties |
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DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Zahra Jahanilomer Saeed Reza farrokhpayam Mohammad Shamsian |
spellingShingle |
Zahra Jahanilomer Saeed Reza farrokhpayam Mohammad Shamsian A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm تحقیقات علوم چوب و کاغذ ایران Keywords: Particleboard modeling GMDH-Type Neural Netwok Physical Mechanical Properties |
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) |
series |
تحقیقات علوم چوب و کاغذ ایران |
issn |
1735-0913 2383-112X |
publishDate |
2014-09-01 |
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. |
topic |
Keywords: Particleboard modeling GMDH-Type Neural Netwok Physical Mechanical Properties |
url |
http://ijwpr.areeo.ac.ir/article_6130_301e53ab2aa4650e82fcb5c248734664.pdf |
work_keys_str_mv |
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