Parameter Optimization of Support Vector Machine Based on Combined Algorithm of QPSO and SA

Support Vector Machine (SVM) is the focus of failure diagnose field. There is not a definite theory to guide the choice of its parameters. In this paper, the analysis and research is done to parameter optimization of SVM. The combined algorithm based on Quantum-behavior Particle Swarm Optimization (QPSO) and Simulated Annealing (SA) is present to optimize the parameters of SVM in order to improve the classification performance of SVM. The comparison of optimization result is done to other algorithms, it testifies that optimization effect of combined algorithm is better.