Hybrid e-regression and validation soft computing techniques: The case of wood dielectric loss factor
This research effort has two orientations. First it aims in the application of hybrid soft computing techniques for the estimation of wood loss factor which is considered a very important feature for wood industry. This has been performed by employing fuzzy weighted Support Vector Machines (SVM), Global SVM and Artificial Neural Networks. The second part of this research focuses in the evaluation of the produced models by employing an innovative fuzzy logic method developed by our research team [10]. For this purpose, experimental data for two different wood species were used. The estimation of the dielectric properties of wood was done by using soft computing algorithms as a function of both ambient electro-thermal conditions applied during drying of wood and basic wood chemistry. The best fit neural models that were developed previously were compared to the current approaches in order to determine the optimal ones.
wood loss factor fuzzy weighted support vector machines artificial neural networks fuzzy evaluation of intelligent systems gaussian support vector machines polynomial support vector machines