DocumentCode
2051928
Title
The kernel proportionate NLMS algorithm
Author
Albu, Felix ; Nishikawa, Kiisa
Author_Institution
Valahia Univ. of Targoviste, Targoviste, Romania
fYear
2013
fDate
9-13 Sept. 2013
Firstpage
1
Lastpage
5
Abstract
In this paper, the kernel proportionate normalized least mean square algorithm (KPNLMS) is proposed. The proportionate factors are used in order to increase the convergence speed and the tracking abilities of the kernel normalized least mean square (KNLMS) adaptive algorithm. We confirm the effectiveness of the proposed algorithm for nonlinear system identification and forward prediction using computer simulations.
Keywords
adaptive filters; least mean squares methods; KPNLMS; Kernel proportionate normalized least mean square algorithm; NLMS algorithm; computer simulations; forward prediction; linear adaptive filters; nonlinear system identification; tracking abilities; Adaptive filters; Convergence; Filtering algorithms; Kernel; Maximum likelihood detection; Nonlinear filters; Prediction algorithms; Kernel normalized least mean square algorithm; forward prediction; nonlinear system identification; proportionate-type algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location
Marrakech
Type
conf
Filename
6811389
Link To Document