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A mathematical model to predict particleboard properties using the GMDH-type neural network and genetic algorithm | ||
| تحقیقات علوم چوب و کاغذ ایران | ||
| Article 4, Volume 29, Fall 3 - Serial Number 48, November 2014, Pages 376-389 PDF (855.99 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwpr.2014.6130 | ||
| Authors | ||
| Zahra Jahanilomer* 1; Saeed Reza farrokhpayam2; Mohammad Shamsian2 | ||
| 1M.Sc., Department of Wood and Paper Science &Technology, Faculty of Natural Resources, University of Zabol, Iran, | ||
| 2Assistant Professor, Department of Wood and Paper science &Technology, Faculty of Natural Resources, University of Zabol, Iran | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Keywords: Particleboard; modeling; GMDH-Type Neural Netwok; Physical Mechanical Properties | ||
| References | ||
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