Paper Info
Title
Performance Evaluation of Particle Swarm Optimization Based Active Noise Control Algorithm
Abstract
Active noise control (ANC) has been used to control low-frequency acoustic noise. The ANC uses an adaptive filter algorithm and normally uses least mean square (LMS) algorithm. The gradient based LMS algorithm suffers from local minima problem. In this paper, particle swarm optimization (PSO) algorithm, which is a non-gradient but simple evolutionary computing type algorithm, is proposed for the ANC system. Detailed mathematical treatment is made and systematic computer simulation studies are carried out to evaluate the performance of the PSO based ANC algorithm.
Year
DOI
Venue
2010
10.1007/978-3-642-17563-3_62
Lecture Notes in Computer Science
Keywords
Field
DocType
adaptive filter,local minima,acoustic noise,active noise control,least mean square,evolutionary computing,computer simulation,low frequency,lms algorithm
Noise,Least mean squares filter,Particle swarm optimization,Excess mean square error,Mathematical optimization,Control theory,Computer science,Algorithm,Evolutionary computation,Maxima and minima,Adaptive filter,Active noise control
Conference
Volume
ISSN
Citations 
6466
0302-9743
2
PageRank 
References 
Authors
0.43
5
3
Name
Order
Citations
PageRank
Nirmal Kumar Rout1243.10
Debi Prasad Das2937.34
Ganapati Panda382466.21