The kernel proportionate NLMS algorithm

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.

[1]  Dinu Coltuc,et al.  An efficient implementation of the kernel affine projection algorithm , 2013, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA).

[2]  Donald L. Duttweiler,et al.  Proportionate normalized least-mean-squares adaptation in echo cancelers , 2000, IEEE Trans. Speech Audio Process..

[3]  Ali H. Sayed,et al.  Fundamentals Of Adaptive Filtering , 2003 .

[4]  Weifeng Liu,et al.  Kernel Adaptive Filtering , 2010 .

[5]  Felix Albu,et al.  New proportionate affine projection sign algorithms , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[6]  Paul Honeine,et al.  Online Prediction of Time Series Data With Kernels , 2009, IEEE Trans. Signal Process..

[7]  Felix Albu.  New Proportionate Affine Projection Algorithm , 2012 .

[8]  Milos Doroslovacki,et al.  Proportionate adaptive algorithms for network echo cancellation , 2006, IEEE Transactions on Signal Processing.

[9]  Kiyoshi Nishikawa,et al.  Mixture structure of kernel adaptive filters for improving the convergence characteristics , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[10]  Shie Mannor,et al.  The kernel recursive least-squares algorithm , 2004, IEEE Transactions on Signal Processing.

[11]  Weifeng Liu,et al.  The Kernel Least-Mean-Square Algorithm , 2008, IEEE Transactions on Signal Processing.