Analysis of the hierarchical LMS algorithm

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 H...

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Published inIEEE signal processing letters Vol. 10; no. 3; pp. 78 - 81
Main Author Nascimento, V.H.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2003
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1070-9908
1558-2361
DOI10.1109/LSP.2002.807863

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Abstract 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.
AbstractList 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.
Author Nascimento, V.H.
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Cites_doi 10.1017/cbo9780511810817
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10.1109/4234.913147
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SubjectTerms Algorithm design and analysis
Algorithms
Computational efficiency
Convergence
Councils
Equivalence
Error analysis
Estimates
Filters
Least squares approximation
Least squares methods
Mathematical models
Optimization
Robustness
Signal processing
Steady-state
Stochastic systems
Title Analysis of the hierarchical LMS algorithm
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