Applying wavelet support vector machine to analog circuit fault diagnosis - 文章详情页

作者：

Zuo, Lei (Zuo, Lei.) | Hou, Ligang (Hou, Ligang.) | Zhang, Wang (Zhang, Wang.) | Wu, Wuchen (Wu, Wuchen.) (学者：吴武臣)

收录：

EI Scopus

摘要：

Based　on　vector　wavelet　kernel　function,　a　method　for　analog　circuit　diagnosis　based　on　genetic　algorithm　(GA)　and　least　squares　wavelet　support　vector　machine　(LSWSVM)　is　proposed.　Using　wavelet　package　as　a　tool　for　extracting　feature,　the　GA-LSWSVM　is　then　applied　to　the　filet　circuit　after　training　by　GA;　the　simulation　results　have　shown　that　the　method　can　enhances　the　accuracy　and　generalization　ability.　©　2010　IEEE.

关键词：

Analog circuits Education computing Engineering education Failure analysis Fault detection Gallium Genetic algorithms Least squares approximations Support vector machines Timing circuits

作者机构：

[ 1 ] [Zuo, Lei]VLSI and System Lab., Beijing University of Technology, Beijing, China
[ 2 ] [Hou, Ligang]VLSI and System Lab., Beijing University of Technology, Beijing, China
[ 3 ] [Zhang, Wang]VLSI and System Lab., Beijing University of Technology, Beijing, China
[ 4 ] [Wu, Wuchen]VLSI and System Lab., Beijing University of Technology, Beijing, China

通讯作者信息：

电子邮件地址：

zuolei2000@sina.com

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