{"title":"常见错误分层NLMS算法","authors":"Mark Raifel, Amos Schreibman, Yaakov Cemal","doi":"10.5281/ZENODO.42308","DOIUrl":null,"url":null,"abstract":"Two-stage common error hierarchical normalized least-mean-square (NLMS) algorithm is presented in the context of network echo cancellers and sparse systems. The suggested adaptive filter structure is generic, uses a common error feedback for both stages, and is applicable with any type of error minimization technique. The simulation results show that the two-stage method exploits the sparseness of the system better than the proportionate NLMS (PNLMS) while keeping the initial convergence rate intact and improving the steady state convergence time significantly.","PeriodicalId":331889,"journal":{"name":"2011 19th European Signal Processing Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Common error hierarchical NLMS algorithm\",\"authors\":\"Mark Raifel, Amos Schreibman, Yaakov Cemal\",\"doi\":\"10.5281/ZENODO.42308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two-stage common error hierarchical normalized least-mean-square (NLMS) algorithm is presented in the context of network echo cancellers and sparse systems. The suggested adaptive filter structure is generic, uses a common error feedback for both stages, and is applicable with any type of error minimization technique. The simulation results show that the two-stage method exploits the sparseness of the system better than the proportionate NLMS (PNLMS) while keeping the initial convergence rate intact and improving the steady state convergence time significantly.\",\"PeriodicalId\":331889,\"journal\":{\"name\":\"2011 19th European Signal Processing Conference\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 19th European Signal Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.42308\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 19th European Signal Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.42308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Two-stage common error hierarchical normalized least-mean-square (NLMS) algorithm is presented in the context of network echo cancellers and sparse systems. The suggested adaptive filter structure is generic, uses a common error feedback for both stages, and is applicable with any type of error minimization technique. The simulation results show that the two-stage method exploits the sparseness of the system better than the proportionate NLMS (PNLMS) while keeping the initial convergence rate intact and improving the steady state convergence time significantly.