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Cited by 375

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Convergence analysis of LMS filters with uncorrelated Gaussian data

Feuer, A., Weinstein, E.·1985·IEEE Transactions on Acoustics, Speech, and Signal Processing

Abstract

Statistical analysis of the least mean-squares (LMS) adaptive algorithm with uncorrelated Gaussian data is presented. Exact analytical expressions for the steady-state mean-square error (mse) and the performance degradation due to weight vector misadjustment are derived. Necessary and sufficient conditions for the convergence of the algorithm to the optimal (Wiener) solution within a finite variance are derived. It is found that the adaptive coefficient μ, which controls the rate of convergence of the algorithm, must be restricted to an interval significantly smaller than the domain commonly stated in the literature. The outcome of this paper, therefore, places fundamental limitations on the mse performance and rate of convergence of the LMS adaptive scheme.

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Metadata

Authors
Feuer, A., & Weinstein, E.
Venue
IEEE Transactions on Acoustics, Speech, and Signal Processing
Published
1985-02
Volume
33
Issue
1
Pages
222-230
ISSN
0096-3518 (ISSN-L)
OpenAlex
W2111411409
Cited by
375
References
4
FWCI
8.66
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Convergence analysis of LMS filters with uncorrelated Gaussian data. https://app.cochat.ai/papers/10.1109/tassp.1985.1164493
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Feuer, A., & Weinstein, E. (1985). Convergence analysis of LMS filters with uncorrelated Gaussian data. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/tassp.1985.1164493

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.