MODELING THE PHYSICAL PROPERTIES OF PARTICLEBOARD FROM CANOLA (BRASSICA NAPUS) STALKS BY USING MLP, RBFN AND ANFIS ARTIFICIAL NEURAL NETWORK

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NAZERIAN M.; AKBARI S.; KERMANIYAN H.; HASHEMI M.

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Journal: Journal of Forest and Wood Products Year:2017 | Volume:70 | Issue:1 Page(s): 179-188

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Title

MODELING THE PHYSICAL PROPERTIES OF PARTICLEBOARD FROM CANOLA (BRASSICA NAPUS) STALKS BY USING MLP, RBFN AND ANFIS ARTIFICIAL NEURAL NETWORK

Author(s)

NAZERIAN M. | AKBARI S. | KERMANIYAN H. | HASHEMI M. | Issue Writer Certificate

Keywords

PARTICLEBOARDQ2

PHYSICAL PROPERTIESQ2

ARTIFICIAL NEURAL NETWORKQ2

Abstract

Different factors influence on the properties of wood composite panels. Evaluating all of these factors not only increases waste of time and energy but also decreases accuracy in estimation of influence value of selected factors in manufacturing panels in order to obtain optimum point of different properties of wood composite panels. Hence, application of a new statistical method is necessary for determination of model estimating production´s optimum point. This study was aimed to evaluate the ARTIFICIAL NEURAL NETWORKs performance to model the PHYSICAL PROPERTIES of the PARTICLEBOARD made of canola stalks particles. The PHYSICAL PROPERTIES of the PARTICLEBOARD were modeled and estimated using different weight ratios of melamine formaldehyde to urea formaldehyde, canola stalks to poplar particles and mat moisture content through MLP, RBFN and ANFIS ARTIFICIAL NEURAL NETWORKs. The results showed that MLP neural network has better performance than RBFN and ANFIS neural networks to estimate the PHYSICAL PROPERTIES of the PARTICLEBOARD. The results also showed that the artificial intelligence models have a proper precision and ability to predict the PARTICLEBOARD's PHYSICAL PROPERTIES. The results of the sensitivity analysis also showed that for estimating TS2 and WA24, the most important parameter was mat moisture content with a positive effect on the modeling, and melamine formaldehyde to urea formaldehyde ratio was also the most effective parameter for estimating TS24 and WA2.

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