{"title":"基于WPD和MLPNN的汽车电磁干扰识别研究","authors":"Yinhan Gao, Xi-lai Ma, Kaiyu Yang, Ruibao Wang","doi":"10.1109/ICIEA.2007.4318830","DOIUrl":null,"url":null,"abstract":"The technique for recognizing and identifying disturbed signals based on wavelet packet decomposition (WPD) and multilayer perceptron neural network (MLPNN) was proposed. It was greatly reduced the volume of computation after Parseval's theorem energy rule and feature extraction of the disturbed signals emerged by the equipment called EM-Test which could bring confirmed automotive interferential signals on automobile. A neural network was also developed for fast interferences identification.","PeriodicalId":231682,"journal":{"name":"2007 2nd IEEE Conference on Industrial Electronics and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Research on Identification for Electromagnetic Interference in Automobile Based on WPD and MLPNN\",\"authors\":\"Yinhan Gao, Xi-lai Ma, Kaiyu Yang, Ruibao Wang\",\"doi\":\"10.1109/ICIEA.2007.4318830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The technique for recognizing and identifying disturbed signals based on wavelet packet decomposition (WPD) and multilayer perceptron neural network (MLPNN) was proposed. It was greatly reduced the volume of computation after Parseval's theorem energy rule and feature extraction of the disturbed signals emerged by the equipment called EM-Test which could bring confirmed automotive interferential signals on automobile. A neural network was also developed for fast interferences identification.\",\"PeriodicalId\":231682,\"journal\":{\"name\":\"2007 2nd IEEE Conference on Industrial Electronics and Applications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 2nd IEEE Conference on Industrial Electronics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2007.4318830\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd IEEE Conference on Industrial Electronics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2007.4318830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Research on Identification for Electromagnetic Interference in Automobile Based on WPD and MLPNN
The technique for recognizing and identifying disturbed signals based on wavelet packet decomposition (WPD) and multilayer perceptron neural network (MLPNN) was proposed. It was greatly reduced the volume of computation after Parseval's theorem energy rule and feature extraction of the disturbed signals emerged by the equipment called EM-Test which could bring confirmed automotive interferential signals on automobile. A neural network was also developed for fast interferences identification.