The Fault Diagnosis Research of Support Vector Machine with Optimized Parameters Based on Genetic Algorithm

摘要

SVM (Support vector Machine), which is based on structural risk minimum principle, overcome the shortness of the traditional machine learning method, especially fit for the small sample problem, it is the focus of the failure diagnose field. There is not a definite theory to guide the choice of SVM parameters. It has great influence on the classification performance and operating speed to choose the proper parameters of SVM. In this paper, GA is utilized to optimize the parameters of SVM and its kernel function to improve the performance by properly choosing the fitness function. Simulation result demonstrates that this method can improve the over-all properties of the failure diagnose system.