A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.