By Topic

Statistical analysis of the LMS algorithm with a zero-memory nonlinearity after the adaptive filter

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Costa, M.H. ; Biomed. Instrum. Group, Univ. Catolica de Pelotas, Pelotas, Brazil ; Bermudez, J.C.M. ; Bershad, N.J.

This paper presents a statistical analysis of the least mean square (LMS) algorithm when a zero-memory nonlinearity appears at the adaptive filter output. The nonlinearity is modelled by a scaled error function. Deterministic nonlinear recursions are derived for the mean weight and mean square error (MSE) behavior for white Gaussian inputs and slow adaptation. Monte Carlo simulations show excellent agreement with the behavior predicted by the theoretical models. The analytical results show that a small nonlinear effect has a significant impact on the converged MSE

Published in:

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on  (Volume:3 )

Date of Conference:

15-19 Mar 1999