Particle swarm optimization for adaptive IIR filter structures

D. J. Krusienski, William Kenneth Jenkins

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

77 Scopus citations

Abstract

This paper introduces the application of particle swarm optimization techniques to infinite impulse response (IIR) adaptive filter structures. Particle swarm optimization (PSO) is similar to the genetic algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. Unlike the genetic algorithm, particle swarm optimization has not emerged in adaptive filtering literature. Both techniques are independent of the adaptive filter structure and are capable of converging on the global solution for multimodal optimization problems, which makes them especially useful for optimizing IIR and nonlinear adaptive filters. This paper outlines PSO and provides a comparison to the GA for IIR filter structures.

Original languageEnglish (US)
Title of host publicationProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages965-970
Number of pages6
StatePublished - Sep 13 2004
EventProceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States
Duration: Jun 19 2004Jun 23 2004

Publication series

NameProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Volume1

Other

OtherProceedings of the 2004 Congress on Evolutionary Computation, CEC2004
CountryUnited States
CityPortland, OR
Period6/19/046/23/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Krusienski, D. J., & Jenkins, W. K. (2004). Particle swarm optimization for adaptive IIR filter structures. In Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 (pp. 965-970). (Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004; Vol. 1).