This paper presents a unifying view of various error nonlinearities that are used in least mean square (LMS) adaptation such as the least mean fourth (LMF) algorithm and its family and the least-mean mixed-norm algorithm. Specifically, it is shown that the LMS algorithm and its error-modified variants are approximations of two previously developed optimum nonlinearities which are expressed in terms of the additive noise probability density function (PDF). This is demonstrated through an approximation of the optimum nonlinearities by expanding the noise PDF in a Gram-Charlier series. Thus a link is established between intuitively proposed and theoretically justified variants of the LMS algorithm. The approximation has also a practical advantage in that it provides a trade-off between simplicity and more accurate realization of the optimum nonlinearities.
|Title of host publication
|Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 1998
|1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998 - Seattle, WA, United States
Duration: May 12 1998 → May 15 1998
|1998 23rd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 1998
|05/12/98 → 05/15/98
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering