{"title":"∈归一化符号回归最小平均四次方(NSRLMF)自适应算法","authors":"Mohammed Mujahid Ulla Faiz, A. Zerguine","doi":"10.1109/ISSPA.2012.6310571","DOIUrl":null,"url":null,"abstract":"In this paper, a new algorithm, the ϵ-normalized sign regressor least mean fourth (NSRLMF) algorithm is presented as a substitute for the ϵ-normalized least mean fourth (NLMF) algorithm. This new algorithm reduces significantly the computational load. Moreover, the proposed algorithm has similar convergence properties as those of the ϵ-NLMF algorithm. Finally, simulations corroborate very well the theoretical findings.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"The ∈-normalized sign regressor least mean fourth (NSRLMF) adaptive algorithm\",\"authors\":\"Mohammed Mujahid Ulla Faiz, A. Zerguine\",\"doi\":\"10.1109/ISSPA.2012.6310571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new algorithm, the ϵ-normalized sign regressor least mean fourth (NSRLMF) algorithm is presented as a substitute for the ϵ-normalized least mean fourth (NLMF) algorithm. This new algorithm reduces significantly the computational load. Moreover, the proposed algorithm has similar convergence properties as those of the ϵ-NLMF algorithm. Finally, simulations corroborate very well the theoretical findings.\",\"PeriodicalId\":248763,\"journal\":{\"name\":\"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPA.2012.6310571\",\"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 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2012.6310571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The ∈-normalized sign regressor least mean fourth (NSRLMF) adaptive algorithm
In this paper, a new algorithm, the ϵ-normalized sign regressor least mean fourth (NSRLMF) algorithm is presented as a substitute for the ϵ-normalized least mean fourth (NLMF) algorithm. This new algorithm reduces significantly the computational load. Moreover, the proposed algorithm has similar convergence properties as those of the ϵ-NLMF algorithm. Finally, simulations corroborate very well the theoretical findings.
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