A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

Shujaat Khan, Muhammad Usman, I. Naseem, R. Togneri, Bennamoun
{"title":"A robust variable step size fractional least mean square (RVSS-FLMS) algorithm","authors":"Shujaat Khan, Muhammad Usman, I. Naseem, R. Togneri, Bennamoun","doi":"10.1109/CSPA.2017.8064914","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).","PeriodicalId":445522,"journal":{"name":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSPA.2017.8064914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16

Abstract

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).
一种鲁棒变步长分数最小均方(RVSS-FLMS)算法
在本文中,我们提出了一个可变步长分数最小均方(FLMS)算法的自适应框架。提出的鲁棒变步长FLMS (RVSS-FLMS)算法通过动态更新FLMS的步长,实现高收敛速度和低稳态误差。为了评估的目的,考虑了系统识别问题。实验结果表明,与自适应步长修正FLMS (AMFLMS)和自适应步长修正FLMS相比,该方法具有更好的收敛速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信