{"title":"经验数据分解滤波设计的一般方法","authors":"Xiaoqin Wu, Zhen Guo, Hongke Zhang","doi":"10.1109/ICOSP.2012.6491591","DOIUrl":null,"url":null,"abstract":"Based on the research on Empirical Data Decomposition (EDD), the structure for Empirical Data Decomposition is proposed in which the high pass filter is composed of a predictor and an adder. In terms of reconstructing requirement, the filter design rule is presented when the EDD analysis filter and synthesis filter are restricted as FIR filter. The relationship between equivalent synthesis filter and analysis filter is also presented. Finally the structure for the synthesis filter is discussed. Except for the FIR requirement, there is no additional restriction for the predictor, so the filter can be easily designed to satisfy different requirements. EDD is suitable not only for stationary data analysis or piece-wise stationary data analysis but also for non-stationary data analysis.","PeriodicalId":143331,"journal":{"name":"2012 IEEE 11th International Conference on Signal Processing","volume":"34 12","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"General method for empirical data decomposition filtering design\",\"authors\":\"Xiaoqin Wu, Zhen Guo, Hongke Zhang\",\"doi\":\"10.1109/ICOSP.2012.6491591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the research on Empirical Data Decomposition (EDD), the structure for Empirical Data Decomposition is proposed in which the high pass filter is composed of a predictor and an adder. In terms of reconstructing requirement, the filter design rule is presented when the EDD analysis filter and synthesis filter are restricted as FIR filter. The relationship between equivalent synthesis filter and analysis filter is also presented. Finally the structure for the synthesis filter is discussed. Except for the FIR requirement, there is no additional restriction for the predictor, so the filter can be easily designed to satisfy different requirements. EDD is suitable not only for stationary data analysis or piece-wise stationary data analysis but also for non-stationary data analysis.\",\"PeriodicalId\":143331,\"journal\":{\"name\":\"2012 IEEE 11th International Conference on Signal Processing\",\"volume\":\"34 12\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 11th International Conference on Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2012.6491591\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 11th International Conference on Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2012.6491591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
General method for empirical data decomposition filtering design
Based on the research on Empirical Data Decomposition (EDD), the structure for Empirical Data Decomposition is proposed in which the high pass filter is composed of a predictor and an adder. In terms of reconstructing requirement, the filter design rule is presented when the EDD analysis filter and synthesis filter are restricted as FIR filter. The relationship between equivalent synthesis filter and analysis filter is also presented. Finally the structure for the synthesis filter is discussed. Except for the FIR requirement, there is no additional restriction for the predictor, so the filter can be easily designed to satisfy different requirements. EDD is suitable not only for stationary data analysis or piece-wise stationary data analysis but also for non-stationary data analysis.