Title :
The kernel proportionate NLMS algorithm
Author :
Albu, Felix ; Nishikawa, Kiisa
Author_Institution :
Valahia Univ. of Targoviste, Targoviste, Romania
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;
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location :
Marrakech
