Shock and Vibration

Shock and Vibration / 2009 / Article

Open Access

Volume 16 |Article ID 587685 | https://doi.org/10.3233/SAV-2009-0472

Ya-li Zhou, Qi-zhi Zhang, Tao Zhang, Xiao-dong Li, Woon-seng Gan, "Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule", Shock and Vibration, vol. 16, Article ID 587685, 10 pages, 2009. https://doi.org/10.3233/SAV-2009-0472

Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

Received13 Jul 2007

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.

Copyright © 2009 Hindawi Publishing Corporation. 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.


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