Referenced Items are not available for this document.
We analyze the hierarchical least mean-square (HLMS) algorithm, providing expressions for its steady-state mean-square error (MSE). We find conditions for the hierarchical structure to be equivalent to the optimal (full-length) Wiener solution. When these conditions are not satisfied, we show that HLMS will compute biased estimates. Our analysis also shows that even when these conditions hold, the MSE obtained using HLMS may be much larger than that obtained using LMS, since the potentially large MSEs at the subfilters in the first hierarchical level directly affect the output MSE.
Published in:
Signal Processing Letters, IEEE
(Volume:10
,
Issue:
3
)
Date of Publication: March 2003
- Page(s):
- 78 - 81
- ISSN :
- 1070-9908
- INSPEC Accession Number:
- 7548953
- Digital Object Identifier :
- 10.1109/LSP.2002.807863
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 28 February 2003
- Issue Date :
- March 2003
- Sponsored by :
- IEEE Signal Processing Society
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