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

D. J. Krusienski, W. K. Jenkins

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 languageEnglish (US)
Pages (from-to)241-245
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume1
StatePublished - 2004

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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