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2009•IEEE Signal Processing MagazineRequires access

Enhanced-Convergence Normalized LMS Algorithm[DSP Tips & Tricks]

M. Givens

Open publisher page 8 citations

Abstract

Least mean square (LMS) algorithms have found great utility in many adaptive filtering applications. This article shows how the traditional constraints placed on the update gain of normalized LMS algorithms are overly restrictive. We present relaxed update gain constraints that significantly improve normalized LMS algorithm convergence speed.

About this research paper

What this paper is about

Least mean square (LMS) algorithms have found great utility in many adaptive filtering applications. This article shows how the traditional constraints placed on the update gain of normalized LMS algorithms are overly restrictive. We present relaxed update gain constraints that significantly improve normalized LMS algorithm convergence speed.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

Least mean square (LMS) algorithms have found great utility in many adaptive filtering applications. This article shows how the traditional constraints placed on the update gain of normalized LMS algorithms are overly restrictive. We present relaxed update gain constraints that significantly improve normalized LMS algorithm convergence speed.

Key concepts: Least mean squares filter, Convergence (economics), Algorithm, Adaptive filter, Computer science, Digital signal processing, Algorithm design, Economics

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