{"title":"一种新的自适应滤波结构:NLMS、DST-LMS和DCT-LMS方案在肌电信号建模中的比较研究","authors":"A. Veiga, Y. Iano, G. Carrijo","doi":"10.1109/PACRIM.2001.953693","DOIUrl":null,"url":null,"abstract":"The goal of this work is to present a transform domain algorithm called discrete sine transform with axis rotation LMS adaptive filter. Based on the least-mean-square (LMS) and discrete sine transform with axis rotation (DSTr) equations, a proposed algorithm is deduced. The authors compare its performance, through computer simulations, with normalized LMS (NLMS), discrete sine transform LMS (DST-LMS) and discrete cosine transform LMS (DCT-LMS) schemes. Another goal of this paper is the study of electromyographic (EMG) signal modeling using the DSTr-LMS algorithm. This is a type of signal that can be represented by an autoregressive model of the fourth order. Using an adaptive filter with adequate order as a predictor, its coefficients can be viewed as a representation of this signal. The learning curves of DSTr-LMS exhibit a better convergence rate and equivalent values of steady state mean-square error (MSE) if compared with learning curves of the other algorithms already mentioned. Therefore, the authors conclude that this set of coefficients is a good representation of this type of signal.","PeriodicalId":261724,"journal":{"name":"2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A new adaptive filter structure: comparative study of NLMS, DST-LMS and DCT-LMS schemes applied to electromyographic signal modelling\",\"authors\":\"A. Veiga, Y. Iano, G. Carrijo\",\"doi\":\"10.1109/PACRIM.2001.953693\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this work is to present a transform domain algorithm called discrete sine transform with axis rotation LMS adaptive filter. Based on the least-mean-square (LMS) and discrete sine transform with axis rotation (DSTr) equations, a proposed algorithm is deduced. The authors compare its performance, through computer simulations, with normalized LMS (NLMS), discrete sine transform LMS (DST-LMS) and discrete cosine transform LMS (DCT-LMS) schemes. Another goal of this paper is the study of electromyographic (EMG) signal modeling using the DSTr-LMS algorithm. This is a type of signal that can be represented by an autoregressive model of the fourth order. Using an adaptive filter with adequate order as a predictor, its coefficients can be viewed as a representation of this signal. The learning curves of DSTr-LMS exhibit a better convergence rate and equivalent values of steady state mean-square error (MSE) if compared with learning curves of the other algorithms already mentioned. Therefore, the authors conclude that this set of coefficients is a good representation of this type of signal.\",\"PeriodicalId\":261724,\"journal\":{\"name\":\"2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PACRIM.2001.953693\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2001 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (IEEE Cat. No.01CH37233)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2001.953693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new adaptive filter structure: comparative study of NLMS, DST-LMS and DCT-LMS schemes applied to electromyographic signal modelling
The goal of this work is to present a transform domain algorithm called discrete sine transform with axis rotation LMS adaptive filter. Based on the least-mean-square (LMS) and discrete sine transform with axis rotation (DSTr) equations, a proposed algorithm is deduced. The authors compare its performance, through computer simulations, with normalized LMS (NLMS), discrete sine transform LMS (DST-LMS) and discrete cosine transform LMS (DCT-LMS) schemes. Another goal of this paper is the study of electromyographic (EMG) signal modeling using the DSTr-LMS algorithm. This is a type of signal that can be represented by an autoregressive model of the fourth order. Using an adaptive filter with adequate order as a predictor, its coefficients can be viewed as a representation of this signal. The learning curves of DSTr-LMS exhibit a better convergence rate and equivalent values of steady state mean-square error (MSE) if compared with learning curves of the other algorithms already mentioned. Therefore, the authors conclude that this set of coefficients is a good representation of this type of signal.