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A New Fast Convergence Adaptive Algorithm

P. Palanisamy, N. Kalyanasundaram

Open publisher page 2 citations

Abstract

In this paper, a new fast convergence adaptive algorithm with variable step size is proposed for FIR adaptive filter. This new proposed algorithm is derived based on the quasi-Newton family. Simulation results are presented to compare the convergence of the proposed algorithm with least mean square (LMS) algorithm and RLS algorithm. It shows that the proposed new algorithm has comparable convergence speed to the other known adaptive algorithms

About this research paper

What this paper is about

In this paper, a new fast convergence adaptive algorithm with variable step size is proposed for FIR adaptive filter. This new proposed algorithm is derived based on the quasi-Newton family. Simulation results are presented to compare the convergence of the proposed algorithm with least mean square (LMS) algorithm and RLS algorithm. It shows that the proposed new algorithm has comparable convergence speed to the other known adaptive algorithms

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, a new fast convergence adaptive algorithm with variable step size is proposed for FIR adaptive filter. This new proposed algorithm is derived based on the quasi-Newton family. Simulation results are presented to compare the convergence of the proposed algorithm with least mean square (LMS) algorithm and RLS algorithm. It shows that the proposed new algorithm has comparable convergence speed to the other known adaptive algorithms

Key concepts: Convergence (economics), Algorithm, Adaptive filter, Computer science, Least mean squares filter, Adaptive algorithm, Algorithm design, Ramer–Douglas–Peucker algorithm

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