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

Abstract EN

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.