A Novel Intelligent Modeling Method for Wood Drying Process

Abstract

This paper investigates the development and intelligent modeling problem for a wood drying kiln process via optimized support vector machine (SVM). Based on parameters optimization and model selection idea, the swarm intelligence algorithms of Particle Swarm Optimization (PSO)-SVM and Genetic Algorithm (GA)-SVM were proposed for wood drying process with strong coupling and nonlinear characteristics. The simulation results showed that both of these two kinds of swarm intelligence optimization algorithm could get the appropriate parameters of SVM effectively, and by contrast, PSO showed a better learning ability and generalization in wood drying process modeling, and could establish predictive model with better accessibility.