TY - GEN
T1 - Correntropy induced metric penalized sparse RLS algorithm to improve adaptive system identification
AU - Gui, Guan
AU - Dai, Linglong
AU - Zheng, Baoyu
AU - Xu, Li
AU - Adachi, Fumiyuki
PY - 2016/7/5
Y1 - 2016/7/5
N2 - Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l-1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).
AB - Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l-1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).
KW - Adaptive system identification.
KW - Correntropy induced metric (CIM)
KW - Recursive least square (RLS)
KW - Sparse adaptive filtering
UR - http://www.scopus.com/inward/record.url?scp=84979790725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84979790725&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2016.7504179
DO - 10.1109/VTCSpring.2016.7504179
M3 - Conference contribution
AN - SCOPUS:84979790725
T3 - IEEE Vehicular Technology Conference
BT - 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 83rd IEEE Vehicular Technology Conference, VTC Spring 2016
Y2 - 15 May 2016 through 18 May 2016
ER -