By Topic

Analysis of the momentum LMS algorithm

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)

Several modifications of the well-known LMS algorithm have been proposed for improved operation. This work analyzes one such algorithm that corresponds to the standard LMS algorithm with an additional update term, parameterized by the scalar factor α where |α|<1. The analysis of convergence yields some novel behavior insofar that it leads to complex eigenvalues of the transition matrix for the mean weight vector. It is demonstrated that the algorithm becomes unstable as |α|→1. Several computer simulation examples support the conclusion that, while the momentum LMS algorithm has smoother convergence, no significant gain in convergence speed over the conventional LMS algorithm can be expected. However, because of this smoothing effect, the MLMS algorithm may be useful in applications where error bursting is a problem. The results presented illustrate some convergence properties of a nonlinear form of the MLMS algorithm, such as that used to train a single-layer perceptron

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:38 ,  Issue: 12 )