{"title":"基于小分量分析的系统建模的有效总最小二乘方法","authors":"Y. Rao, J. Príncipe","doi":"10.1109/NNSP.2002.1030037","DOIUrl":null,"url":null,"abstract":"We present two algorithms to solve the total least-squares (TLS) problem. The algorithms are on-line with O(N/sup 2/) and O(N) complexity. The convergence of the algorithms is significantly faster than the traditional methods. A mathematical analysis of convergence is also provided along with simulations to substantiate the claims. We also apply the TLS algorithms for FIR system identification with known model order in the presence of noise.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Efficient total least squares method for system modeling using minor component analysis\",\"authors\":\"Y. Rao, J. Príncipe\",\"doi\":\"10.1109/NNSP.2002.1030037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present two algorithms to solve the total least-squares (TLS) problem. The algorithms are on-line with O(N/sup 2/) and O(N) complexity. The convergence of the algorithms is significantly faster than the traditional methods. A mathematical analysis of convergence is also provided along with simulations to substantiate the claims. We also apply the TLS algorithms for FIR system identification with known model order in the presence of noise.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2002.1030037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2002.1030037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient total least squares method for system modeling using minor component analysis
We present two algorithms to solve the total least-squares (TLS) problem. The algorithms are on-line with O(N/sup 2/) and O(N) complexity. The convergence of the algorithms is significantly faster than the traditional methods. A mathematical analysis of convergence is also provided along with simulations to substantiate the claims. We also apply the TLS algorithms for FIR system identification with known model order in the presence of noise.
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