Recursive regularisation parameter selection for sparse RLS algorithm

In this Letter, the authors propose a recursive regularisation parameter selection method for sparse recursive least squares (RLS) algorithm. The proposed RLS algorithm is regularised by a convex function, which equals the linear combination of two convex functions, one to cope with random sparsity,...

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Veröffentlicht in:	Electronics letters Jg. 54; H. 5; S. 286 - 287
Hauptverfasser:	Sun, Dajun, Liu, Lu, Zhang, Youwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 08.03.2018
Schlagworte:	adaptive filtering adaptive filters Circuits and systems convex regularised penalty function group sparsity l0‐norm l2,0‐norm least squares approximations random sparsity recursive regularisation parameter selection method sparse recursive least squares algorithm sparse RLS algorithm time‐varying sparse system sparse recursive least squares algorithm adaptive filters least squares approximations l0-norm adaptive filtering recursive regularisation parameter selection method random sparsity sparse RLS algorithm l2,0-norm group sparsity convex regularised penalty function time-varying sparse system
ISSN:	0013-5194, 1350-911X, 1350-911X
Online-Zugang:	Volltext
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