Study of Fault Diagnosis Method Based on Ensemble-Multi-SVM Classifiers

摘要：

　　In order to improve the system accuracy of fault diagnosis,this paper proposes the integrated fault diagnosis method based on multi-SVM classifiers.MultiBoost integrated learning method using the AdaBoost algorithm and Wagging algorithm composed of multiple integrated with a combination of base classifiers to improve the classification accuracy of the system.The simulation results show that the method used in network fault diagnosis system of classification module design,making fault diagnosis accuracy has been significantly improved.

关键词： Fault Diagnosis Ensemble Learning Support Vector Machines Classification

作者: LV Feng LI Xiang SUN Hao DU Hailian RONG Wenjie

作者单位: Electronic Department,Hebei Normal University,Shijiazhuang 050024,P.R.China Shijiazhuang Power Supply Company,Shijiazhuang 050010,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 3272-3276

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）