IDR Logo

Please use this identifier to cite or link to this item:
Title: Particle swarm optimization based active noise control algorithm without secondary path identification
Authors: Rout N.K.
Das D.P.
Panda G.
Keywords: Active noise control (ANC)
adaptive filtering
conditional reinitialized PSO (CRPSO)
particle swarm optimization (PSO)
Issue Date: 2012
Citation: 37
Abstract: In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm. � 2006 IEEE.
Appears in Collections:Research Publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.