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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Vydané v:	Electronics letters Ročník 54; číslo 5; s. 286 - 287
Hlavní autori:	Sun, Dajun, Liu, Lu, Zhang, Youwen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 08.03.2018
Predmet:	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
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Shrnutí:	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, and the other to cope with group sparsity. The normal equations corresponding to the RLS algorithm with the proposed convex regularised penalty function are derived, and a recursive algorithm to update the regularisation parameters (i.e. the coefficients of the linear combination) is proposed. As an example, by using the linear combination of an $l_0$l0-norm and an $l_{2\comma 0}$l2,0-norm as the penalty function, simulation results show that the proposed sparse RLS with recursive regularisation parameter selection can achieve better performance in terms of mean square error for a slowly time-varying sparse system with both random sparsity and group sparsity.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2017.4242