Premium
Mixed norm regularized recursive total least squares for group sparse system identification
Author(s) -
Lim Junseok,
Pang HeeSuk
Publication year - 2016
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2635
Subject(s) - real time locating system , recursive least squares filter , algorithm , least squares function approximation , system identification , identification (biology) , mathematics , regularization (linguistics) , norm (philosophy) , mathematical optimization , computer science , statistics , artificial intelligence , adaptive filter , data mining , botany , real time computing , estimator , biology , political science , law , measure (data warehouse)
Summary A mixed l p ,0 ‐regularized recursive total least squares (RTLS) algorithm is considered for group sparse system identification. Regularized recursive least squares (RLS) has been successfully applied to group sparse system identification; however, the estimation performance in regularized RLS‐based algorithms deteriorates when both input and output are contaminated by noise (the error‐in‐variables problem). We propose an l p ,0 ‐RTLS algorithm to handle group sparse system identification with errors‐in‐variables. The proposed algorithm is an RLS‐like solution that utilizes l p ,0 ‐regularization. The proposed algorithm provides excellent performance as well as reduces the required complexity by effective inversion matrix handling. Simulations demonstrate the superiority of the proposed l p ,0 ‐regularized RTLS for a group sparse system identification setting. Copyright © 2015 John Wiley & Sons, Ltd.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom