Parameter optimization algorithm of SVM for fault classification in traction converter
The classification performance of Support Vector Machine(SVM)is heavily influenced by its kernel parameter g and penalty factor c.in this paper,Cross-validation(CV)based grid-search optimization,CV-based genetic algorithm(GA)and CV-based particle swarm optimization(PSO)are respectively used for parameters optimization in SVM for fault classification of inverters in traction converter.Simulation result shows that SVM can reach the highest classification accuracy by using CV-based grid-search optimization algorithm,and it has been proved to be practical to use CV-based grid-search as SVMs parameters optimization algorithm for fault classification in traction converter.
SVM fault classification parameter optimization traction converter
Zhao Jin Wu Chaorong Huang Chengguang Wu Feng
School of automation,HuaZhong University of Science and Technology,Wuhan 430074
国际会议
长沙
英文
3786-3791
2014-05-31(万方平台首次上网日期,不代表论文的发表时间)