Nonlinear fitting calculation of wood thermal conductivity using neural networks

A computational model based on artificial neural networks(ANN) was proposed to find the nonlinear variance regulation of the thermal conductivity versus physical properties of wood.The temperature and the porosity of wood were set as the two inputs and the thermal conductivity of wood was set as the output. The number of neurons within the hidden layer varied from three to eight with increment of one,resulting in a total of six networks.The thermal conductivity of birch was predicted using these six networks respectively.The optimal network was recognized as the one which had six neurons within the hidden layer by comparison and analysis of the errors.The mean relative error was 0.21%,and the mean absolute error was 4.33×10-4 W/(m·K).This network was used to predict thermal conductivities of birch at different temperature and porosity.Results demonstrated good agreement between the predicted and the available experimental data,which showed that the ANN model can be used to effectively predict the thermal conductivity of wood and that it has ideal accuracy.