Active noise control using a functional link artificial neural network with the simultaneous perturbation learning rule

Article type: Research Article

Authors: Zhou, Ya-li | Zhang, Qi-zhi | Zhang, Tao | Li, Xiao-dong | Gan, Woon-seng

Affiliations: Department of Computer Science and Automation, Beijing Institute of Machinery, P.O. Box 2865, Beijing, 100192, P.R. China | Institute of Acoustics, Academia Sinica, P.R. China | School of EEE, Nanyang Technological University, Singapore

Note: [] Corresponding author. E-mail: [email protected]

Abstract: In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

Keywords: Active noise control, FLANN, SPSA

DOI: 10.3233/SAV-2009-0472

Journal: Shock and Vibration, vol. 16, no. 3, pp. 325-334, 2009