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Advances in Acoustics and Vibration
Volume 2008 (2008), Article ID 791050, 12 pages
Research Article

Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS)

1Department of Physics and Astronomy, College of Physical and Mathematical Sciences, N283 ESC, Brigham Young University, Provo, UT 84602, USA
2Department of Mechanical Engineering, Ira A. Fulton College of Engineering and Technology, 435 CTB, Brigham Young University, Provo, UT 84602, USA

Received 12 December 2007; Accepted 7 March 2008

Academic Editor: Marek Pawelczyk

Copyright © 2008 Stephan P. Lovstedt et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The FXLMS algorithm, used extensively in active noise control (ANC), exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.