文献类型：会议论文

英文题名：Support Vector Regression and Immune Clone Selection Algorithm for Intelligent Electronic Circuit Fault Diagnosis

作者：Tian, WenJie[1];Liu, JiCheng[1];Geng, Yu[1];Ai, Lan[1]

通讯作者：Tian, WJ[1]

机构：[1]Beijing Union Univ, Automat Inst, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Automat Inst, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：Pacific-Asia Conference on Circuits, Communications and Systems

会议日期：MAY 16-17, 2009

会议地点：Chengdu, PEOPLES R CHINA

语种：英文

外文关键词：fault diagnosis; rough set; immune clone selection algorithm; support vector regression; electronic circuit

摘要：In the analysis of electronic circuit fault diagnosis based on support vector regression (SVR), irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper used rough sets as a preprocessor of SVR to select a subset of input variables and employed the immune clone selection algorithm (ICSA) to optimize the parameters of SVR. Additionally, the proposed ICSA-SVR model that can automatically determine the optimal parameters was tested on the prediction of electronic circuit fault. Then, we compared the proposed ICSA-SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.