A particle swarm optimization - Least mean squares algorithm for adaptive filtering

D. J. Krusienski; W. K. Jenkins

A particle swarm optimization - Least mean squares algorithm for adaptive filtering

D. J. Krusienski, W. K. Jenkins

Research output: Contribution to journal › Conference article › peer-review

Abstract

A particle swarm optimization-least mean squares (PSO-LMS) algorithm is presented for adapting various classes of filter structures. The LMS algorithm is widely accepted as the preeminent adaptive filtering algorithm because of its speed, efficiency, and provably convergent local search capabilities. However, for multimodal error surfaces, a global search algorithm, such as PSO or the genetic algorithm (GA), is required. The proposed PSO-LMS hybrid algorithm combines the advantageous properties of the two conventional algorithms to provide enhanced performance characteristics.

Original language	English (US)
Pages (from-to)	241-245
Number of pages	5
Journal	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	1
State	Published - 2004
Event	Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: Nov 7 2004 → Nov 10 2004

All Science Journal Classification (ASJC) codes

Signal Processing
Computer Networks and Communications

Cite this

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title = "A particle swarm optimization - Least mean squares algorithm for adaptive filtering",

abstract = "A particle swarm optimization-least mean squares (PSO-LMS) algorithm is presented for adapting various classes of filter structures. The LMS algorithm is widely accepted as the preeminent adaptive filtering algorithm because of its speed, efficiency, and provably convergent local search capabilities. However, for multimodal error surfaces, a global search algorithm, such as PSO or the genetic algorithm (GA), is required. The proposed PSO-LMS hybrid algorithm combines the advantageous properties of the two conventional algorithms to provide enhanced performance characteristics.",

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year = "2004",

language = "English (US)",

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journal = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

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publisher = "IEEE Computer Society",

note = "Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers ; Conference date: 07-11-2004 Through 10-11-2004",

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T1 - A particle swarm optimization - Least mean squares algorithm for adaptive filtering

AU - Krusienski, D. J.

AU - Jenkins, W. K.

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N2 - A particle swarm optimization-least mean squares (PSO-LMS) algorithm is presented for adapting various classes of filter structures. The LMS algorithm is widely accepted as the preeminent adaptive filtering algorithm because of its speed, efficiency, and provably convergent local search capabilities. However, for multimodal error surfaces, a global search algorithm, such as PSO or the genetic algorithm (GA), is required. The proposed PSO-LMS hybrid algorithm combines the advantageous properties of the two conventional algorithms to provide enhanced performance characteristics.

AB - A particle swarm optimization-least mean squares (PSO-LMS) algorithm is presented for adapting various classes of filter structures. The LMS algorithm is widely accepted as the preeminent adaptive filtering algorithm because of its speed, efficiency, and provably convergent local search capabilities. However, for multimodal error surfaces, a global search algorithm, such as PSO or the genetic algorithm (GA), is required. The proposed PSO-LMS hybrid algorithm combines the advantageous properties of the two conventional algorithms to provide enhanced performance characteristics.

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M3 - Conference article

AN - SCOPUS:21644489074

SN - 1058-6393

VL - 1

SP - 241

EP - 245

JO - Conference Record - Asilomar Conference on Signals, Systems and Computers

JF - Conference Record - Asilomar Conference on Signals, Systems and Computers

T2 - Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers

Y2 - 7 November 2004 through 10 November 2004

ER -

A particle swarm optimization - Least mean squares algorithm for adaptive filtering

Abstract

All Science Journal Classification (ASJC) codes

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