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2017•Unpublished venueRequires access

New LMS Adaptive Filtering Algorithm with Variable Step Size

Yuan Zhang, Songtao Xi

Open publisher page 15 citations

Abstract

By building a nonlinear function relationship between μ and the error signal e(n) ,this paper presents a new variable step size LMS(Least-Mean-Square)adaptive filtering algorithm, and analyzes the algorithm with various parameters α and β.This step size algorithm avoids the shortage of adjusting step size of SVSLMS (variable step size LMS based on Sigmoid function). Also in the process of the adaptive steady state it has the virtue of e(n) slightly changing close to zero. Theoretical analysis and computer simulations show that with the proposed algorithm, convergence rate can be improved than the former.

About this research paper

What this paper is about

By building a nonlinear function relationship between μ and the error signal e(n) ,this paper presents a new variable step size LMS(Least-Mean-Square)adaptive filtering algorithm, and analyzes the algorithm with various parameters α and β.This step size algorithm avoids the shortage of adjusting step size of SVSLMS (variable step size LMS based on Sigmoid function). Also in the process of the adaptive steady state it has the virtue of e(n) slightly changing close to zero. Theoretical analysis and computer simulations show that with the proposed algorithm, convergence rate can be improved than the former.

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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

By building a nonlinear function relationship between μ and the error signal e(n) ,this paper presents a new variable step size LMS(Least-Mean-Square)adaptive filtering algorithm, and analyzes the algorithm with various parameters α and β.This step size algorithm avoids the shortage of adjusting step size of SVSLMS (variable step size LMS based on Sigmoid function). Also in the process of the adaptive steady state it has the virtue of e(n) slightly changing close to zero. Theoretical analysis and computer simulations show that with the proposed algorithm, convergence rate can be improved than the former.

Key concepts: Least mean squares filter, Sigmoid function, Adaptive filter, Algorithm, Convergence (economics), Variable (mathematics), Economic shortage, Rate of convergence

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