2011Unpublished venueRequires access

An Improved Variable Step-Size LMS Algorithm

Tingting Li, Min Shi, Qingming Yi

Open publisher page 15 citations

Abstract

To improve the performance of LMS adaptive algorithm, in this paper a new variable step-size LMS algorithm is proposed based on the analysis of some variable step-size algorithms. Through establishing a new nonlinear relationship between the step size and the error, the algorithm eliminates the irrelevant noise and improves the convergence rate to obtain a better stability. And the computer simulation results are consistent with the theoretical analysis, which confirmed that the algorithm is superior to other algorithms on convergence rate, tracking speed and steady-state error.

About this research paper

What this paper is about

To improve the performance of LMS adaptive algorithm, in this paper a new variable step-size LMS algorithm is proposed based on the analysis of some variable step-size algorithms. Through establishing a new nonlinear relationship between the step size and the error, the algorithm eliminates the irrelevant noise and improves the convergence rate to obtain a better stability. And the computer simulation results are consistent with the theoretical analysis, which confirmed that the algorithm is superior to other algorithms on convergence rate, tracking speed and steady-state error.

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OpenAlex reports 15 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

To improve the performance of LMS adaptive algorithm, in this paper a new variable step-size LMS algorithm is proposed based on the analysis of some variable step-size algorithms. Through establishing a new nonlinear relationship between the step size and the error, the algorithm eliminates the irrelevant noise and improves the convergence rate to obtain a better stability. And the computer simulation results are consistent with the theoretical analysis, which confirmed that the algorithm is superior to other algorithms on convergence rate, tracking speed and steady-state error.

Key concepts: Convergence (economics), Algorithm, Variable (mathematics), Rate of convergence, Computer science, Stability (learning theory), Nonlinear system, Noise (video)

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