NDLI: Are ensemble-average learning curves reliable in evaluating the performance of adaptive filters?

Content Provider	IEEE Xplore Digital Library
Author	Nascimento, V.H. Sayed, A.H.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA (Nascimento, V.H.)
Abstract	We treat the computation of the learning curves of the LMS algorithm by simulation (that is, the computation of the MSE as a function of the time instant). Since closed-form analytic expressions for learning curves are quite hard to obtain in most practical situations, one usually approximates learning curves by performing several repeated experiments and by averaging the resulting squared-error curves. We show, both by examples and analytically, that when the step-size is large, this approximation of the MSE can be misleading. This is contrary to what one would expect, given the excellent agreement one obtains between simulations and theory for small step-sizes and independent inputs, even using only as few as 10 experiments. The theoretical analysis explains both the good results obtained for small step-sizes, and the discrepancies that arise for large step-sizes.
Starting Page	1171
Ending Page	1175
File Size	478089
Page Count	5
File Format	PDF
ISBN	0780351487
ISSN	10586393
DOI	10.1109/ACSSC.1998.751511
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-11-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive filters Least squares approximation Computational modeling Performance analysis Time measurement Signal generators Noise measurement Noise generators Recursive estimation Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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