DocumentCode :
409678
Title :
Overcoming the independence assumption in LMS filtering
Author :
Rupp, Markus ; Butterweck, Hans-Juergen
Author_Institution :
Inst. fur Nachrichtentechnik und Hochfrequenztechnik, Technische Univ. Wien, Vienna, Austria
Volume :
1
fYear :
2003
fDate :
9-12 Nov. 2003
Firstpage :
607
Abstract :
The learning process of the LMS algorithm remains understood only very poorly. Despite three decades of intensive research, very few results have been found to overcome the classical independence assumption in which the sequence of driving regression vectors is assumed to be statistically independent. While giving relatively precise results for processes of little correlation, the results obtained in other cases are far off from the true values. In this paper, a new approach is taken to investigate the learning behavior of the LMS algorithm using much milder conditions than in the classical independence theory. It is shown that our conditions lead to much better results, in particular for correlated driving processes when compared with the classical independence assumption.
Keywords :
filtering theory; least mean squares methods; statistical analysis; classical independence theory; correlated driving process; driving regression vector; independence assumption; learning process; least mean square filtering; Convergence; Filtering; Filters; History; Lead compounds; Least squares approximation; Stability; Steady-state; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on
Print_ISBN :
0-7803-8104-1
Type :
conf
DOI :
10.1109/ACSSC.2003.1291983
Filename :
1291983
Link To Document :
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