Particle swarm optimization for adaptive IIR filter structures

D. J. Krusienski; William Kenneth Jenkins

Particle swarm optimization for adaptive IIR filter structures

D. J. Krusienski, William Kenneth Jenkins

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 language	English (US)
Title of host publication	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Pages	965-970
Number of pages	6
State	Published - Sep 13 2004
Event	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 - Portland, OR, United States Duration: Jun 19 2004 → Jun 23 2004

Publication series

Name	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Volume	1

Other

Other	Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Country/Territory	United States
City	Portland, OR
Period	6/19/04 → 6/23/04

All Science Journal Classification (ASJC) codes

Engineering(all)

Cite this

@inproceedings{4aa7d86902a941dda3f904c90f405f6d,

title = "Particle swarm optimization for adaptive IIR filter structures",

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.",

author = "Krusienski, {D. J.} and Jenkins, {William Kenneth}",

year = "2004",

month = sep,

day = "13",

language = "English (US)",

isbn = "0780385152",

series = "Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004",

pages = "965--970",

booktitle = "Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004",

note = "Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004 ; Conference date: 19-06-2004 Through 23-06-2004",

}

Krusienski, DJ & Jenkins, WK 2004, Particle swarm optimization for adaptive IIR filter structures. in Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004. Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, vol. 1, pp. 965-970, Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, Portland, OR, United States, 6/19/04.

TY - GEN

T1 - Particle swarm optimization for adaptive IIR filter structures

AU - Krusienski, D. J.

AU - Jenkins, William Kenneth

PY - 2004/9/13

Y1 - 2004/9/13

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=4344698274&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4344698274&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:4344698274

SN - 0780385152

SN - 9780780385153

T3 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

SP - 965

EP - 970

BT - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

T2 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004

Y2 - 19 June 2004 through 23 June 2004

ER -

Particle swarm optimization for adaptive IIR filter structures

Abstract

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