Parameter optimization algorithm of SVM for fault classification in traction converter

作     者：Zhao Jin Wu Chaorong Huang Chengguang Wu Feng 

作者单位：School of automationHuaZhong University of Science and Technology 

会议名称：《第26届中国控制与决策会议》

会议日期：2014年

学科分类：12[管理学] 080801[工学-电机与电器] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Nature Science Foundation under Grant(NO61273174 and NO61034006) 

关 键 词：SVM fault classification parameter optimization traction converter 

摘      要：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 SVM’s parameters optimization algorithm for fault classification in traction converter.