By Topic

Transient and tracking performance analysis of the quantized LMS algorithm for time-varying system identification

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

2 Author(s)
Bermudez, J.C.M. ; Dept. of Electr. Eng., Univ. Federal de Santa Catarina, Florianapolis, Brazil ; Bershad, N.J.

This paper investigates the statistical behavior of the finite precision LMS adaptive filter in the identification of an unknown time-varying stochastic system. Nonlinear recursions are derived for the mean and mean-square behavior of the adaptive weights. Transient and tracking algorithm performance curves are generated from the recursions and shown to be in excellent agreement with Monte Carlo simulations. Our results demonstrate that linear models are inappropriate for analyzing the transient and the steady-state algorithm behavior. The performance curves indicate that the transient and tracking capabilities cannot be determined from perturbations about the infinite precision case. It is shown that the transient phase of the algorithm increases as the digital wordlength or the speed of variation of the unknown system decrease. Design examples illustrate how the theory can be used to select the algorithm step size and the number of bits in the quantizer

Published in:

Signal Processing, IEEE Transactions on  (Volume:44 ,  Issue: 8 )