Genetic Optimization in Nonlinear Systems for Active Noise Control: Accuracy and Performance Evaluation

This paper investigates the performance of genetic optimization in a nonlinear system for active noise control based on Volterra filters. While standard filtered-X algorithms can converge to local minima, genetic algorithms may handle this problem efficiently. In addition, this class of algorithms does not require the identification of the secondary paths. Computer simulations show that the proposed approach gives more accurate results than other techniques in the literature