TY - JOUR
T1 - A particle swarm optimization - Least mean squares algorithm for adaptive filtering
AU - Krusienski, D. J.
AU - Jenkins, W. K.
PY - 2004
Y1 - 2004
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.
UR - http://www.scopus.com/inward/record.url?scp=21644489074&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=21644489074&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:21644489074
SN - 1058-6393
VL - 1
SP - 241
EP - 245
JO - Conference Record of the Asilomar Conference on Signals, Systems and Computers
JF - Conference Record of the Asilomar Conference on Signals, Systems and Computers
ER -