A modified particle swarm optimization algorithm for adaptive filtering

Author

Krusienski, D.J. ; Jenkins, W.K.

Author_Institution

Dept. of Electr. Eng., Pennsylvania State Univ.

fYear

2006

fDate

21-24 May 2006

Lastpage

140

Abstract

Recently particle swarm optimization (PSO) has been studied for use in adaptive filtering problems where the mean squared error (MSE) surface is ill-conditioned. Although the swarm generally converges to a limit point, when the population of the swarm is small the entire swarm often stagnates before reaching the global minimum on the MSE surface. This paper examines enhancements designed to improve the performance of the conventional PSO algorithm. It is shown that an enhanced PSO algorithm, called the Modified PSO (MPSO) algorithm, is quite effective in achieving global convergence for IIR and nonlinear adaptive filters

Keywords

IIR filters; adaptive filters; nonlinear filters; particle swarm optimisation; IIR filters; adaptive filtering problems; adaptive filters; mean squared error surface; modified PSO algorithm; nonlinear filters; particle swarm optimization algorithm; Acceleration; Adaptive filters; Algorithm design and analysis; Convergence; Filtering algorithms; IIR filters; Particle swarm optimization; Surface fitting; Tellurium; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on

Conference_Location

Island of Kos

Print_ISBN

0-7803-9389-9

Type

conf

DOI

10.1109/ISCAS.2006.1692541

Filename

1692541