Artificial Neural Network and Mathematical Modelling Comparative Analysis of Nonisothermal Diffusion of Moisture in Wood

Avramidis, Stavros and Wu, Hongwei (2007) Artificial Neural Network and Mathematical Modelling Comparative Analysis of Nonisothermal Diffusion of Moisture in Wood. Holz als Roh-und Werkstoff, 65 (2). pp. 89-93. ISSN 0018-3768

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Abstract

The objective of this study was to develop an optimum artificial neural network (ANN) capable of predicting the direction and magnitude of the moisture flux through wood under nonisothermal steady-state diffusion. A comparison between experimental measurements and the predicted values of three mathematical models reported in the literature and of the proposed neural network is presented and discussed. When developing the ANN model, several configurations were evaluated. The optimal ANN model was found to be a network with six neurons in one hidden layer. This well-trained network correlated the forecasted to the experimental data with low-level errors compared to previously developed models and also predicted the flux-reversal phenomenon thus confirming that ANN modeling has a much better predictive performance. It was also shown that the numbers of the training data were linked to the performance of the network during estimation. However, the powerful predictive capacity of this modeling method was still supported although a limited experimental data set was trained.

Item Type:	Article
Subjects:	D500 Forestry F200 Materials Science
Department:	Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User:	Hongwei Wu
Date Deposited:	20 Nov 2015 15:20
Last Modified:	12 Oct 2019 23:11
URI:	http://nrl.northumbria.ac.uk/id/eprint/24634

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