Improved Variable Forgetting Factor Proportionate RLS Algorithm with Sparse Penalty and Fast Implementation Using DCD Iterations

the proportionate recursive least squares (PRLS) algorithm has shown faster convergence and better performance than both proportionate updating (PU) mechanism based least mean squares (LMS) algorithms and RLS algorithms with a sparse regularization term. In this paper, we propose a variable forgetti...

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Bibliographic Details
Published in:	China communications Vol. 21; no. 10; pp. 1 - 12
Main Authors:	Zhen, Han, Fengrui, Zhang, Yu, Zhang, Yanfeng, Han, Peng, Jiang
Format:	Journal Article
Language:	English
Published:	China Institute of Communications 01.10.2024 GNSS Research Center,Wuhan University,Wuhan 430072,China%School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
Subjects:	Adaptive filters dichotomous coordinate descent Filtering proportionate matrix RLS Signal to noise ratio Sparse matrices sparse systems Steady-state System identification variable forgetting factor Vectors RLS propor-tionate matrix variable forget-ting factor sparse systems dichotomous coordinate descent
ISSN:	1673-5447
Online Access:	Get full text
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