A modified particle swarm optimization algorithm for adaptive filtering

D. J. Krusienski, W. K. Jenkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

40 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings
Pages137-140
Number of pages4
StatePublished - 2006
EventISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece
Duration: May 21 2006May 24 2006

Other

OtherISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
CountryGreece
CityKos
Period5/21/065/24/06

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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    Krusienski, D. J., & Jenkins, W. K. (2006). A modified particle swarm optimization algorithm for adaptive filtering. In ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings (pp. 137-140). [1692541]