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.

关键词： Support vector machine (SVM) Genetic algorithm (GA) optimization fault diagnosis

作者: SHI Yan LI Xiao-Min QI Xiao-Hui LIANG Xiang

作者单位: Department of Optics and Electrics Engineering,Ordnance Engineering College,Shijiazhuang China Scientific Research Department of Unit 93469 Shijiazhuang China

会议类型: 国际会议

会议名称: 第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)

会议地点: 重庆

会议语种:英文

页码: 1458-1461

在线出版日期: 2009-08-01（万方平台首次上网日期，不代表论文的发表时间）