active noise control using a functional link artificial neural network with the simultaneous perturbation learning rule
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2009
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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.
| Reference Key |
zhou2009shockactive
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|---|---|
| Authors | ;Ya-li Zhou;Qi-zhi Zhang;Tao Zhang;Xiao-dong Li;Woon-seng Gan |
| Journal | Nano letters |
| Year | 2009 |
| DOI |
10.3233/SAV-2009-0472
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