DocumentCode
1545581
Title
Combined LMS/F algorithm
Author
Lim, Shao-Jen ; Harris, J.G.
Author_Institution
Comput. Neuro-Eng. Lab., Florida Univ., Gainesville, FL, USA
Volume
33
Issue
6
fYear
1997
fDate
3/13/1997 12:00:00 AM
Firstpage
467
Lastpage
468
Abstract
A new adaptive filter algorithm has been developed that combines the benefits of the least mean square (LMS) and least mean fourth (LMF) methods. This algorithm, called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjustment. While LMF outperforms LMS for certain noise profiles, its stability cannot be guaranteed for known input signals even For very small step sizes. However, both LMS and LMS/F have good stability properties and LMS/F only adds a few more computations per iteration compared to LMS. Simulations of a non-stationary system identification problem demonstrate the performance benefits of the LMS/F algorithm
Keywords
adaptive filters; convergence of numerical methods; filtering theory; identification; least mean squares methods; adaptive filter algorithm; combined LMS/F algorithm; constant convergence rate; constant misadjustment; least mean fourth method; least mean square method; noise profiles; nonstationary system identification problem; stability properties;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:19970311
Filename
585044
Link To Document