2012Unpublished venueRequires access

A New Variable Step Size LMS Adaptive Filtering Algorithm

Wei Ao, Wan-Qin Xiang, Youpeng Zhang, Lei Wang, Chun-Ying Lv, Zhenghua Wang

Open publisher page 62 citations

Abstract

Focus on the inconsistency between the convergence rate and the steady state error in LMS algorithm, this paper proposed a new variable step size LMS(VSS-LMS) algorithm, by constructing a nonlinear function between the step factor µ and the error signal e(n). The algorithm which modified the weight vector appropriately achieving the optimal by adjusting the step size parameters, could improve the convergence rate and steady-state error performance effectively. The simulation analysis showed that, this algorithm had faster convergence rate speed and smaller steady-state maladjustment compared with the basic LMS algorithm and parts of the similar variable step size algorithm, it fatherly validated this algorithm was superior to existing algorithms.

About this research paper

What this paper is about

Focus on the inconsistency between the convergence rate and the steady state error in LMS algorithm, this paper proposed a new variable step size LMS(VSS-LMS) algorithm, by constructing a nonlinear function between the step factor µ and the error signal e(n). The algorithm which modified the weight vector appropriately achieving the optimal by adjusting the step size parameters, could improve the convergence rate and steady-state error performance effectively. The simulation analysis showed that, this algorithm had faster convergence rate speed and smaller steady-state maladjustment compared with the basic LMS algorithm and parts of the similar variable step size algorithm, it fatherly validated this algorithm was superior to existing algorithms.

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OpenAlex reports 62 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Focus on the inconsistency between the convergence rate and the steady state error in LMS algorithm, this paper proposed a new variable step size LMS(VSS-LMS) algorithm, by constructing a nonlinear function between the step factor µ and the error signal e(n). The algorithm which modified the weight vector appropriately achieving the optimal by adjusting the step size parameters, could improve the convergence rate and steady-state error performance effectively. The simulation analysis showed that, this algorithm had faster convergence rate speed and smaller steady-state maladjustment compared with the basic LMS algorithm and parts of the similar variable step size algorithm, it fatherly validated this algorithm was superior to existing algorithms.

Key concepts: Convergence (economics), Rate of convergence, Algorithm, Adaptive filter, Least mean squares filter, Variable (mathematics), Steady state (chemistry), Computer science

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