Abstract
The normalized least mean square (NLMS) algorithm is known to result in a faster convergence than the least mean square (LMS) algorithm but at the expense of a larger steady-state error. A time-varying normalized mixed-norm LMS-least mean fourth (LMF) algorithm is presented in this work to preserve the fast convergence of the NLMS algorithm while resulting in a lower steady-state error. The simulation results show that a substantial improvement, in both convergence time and steady state error, can be obtained with this mixed-norm algorithm.
Original language | English |
---|---|
Article number | 7072073 |
Journal | European Signal Processing Conference |
Volume | 2002-March |
State | Published - 27 Mar 2002 |
Bibliographical note
Publisher Copyright:© 2002 EUSIPCO.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering