Abstract
In practical active noise control (ANC) systems, nonlinear active controllers may be required in cases where the actuators used in ANC systems or the structures to be controlled exhibit nonlinear characteristics. In this paper, a chebyschev functional link artificial neural network (C-FLANN) is used as a nonlinear controller. Compared with the multilayer perception (MLP) neural network, C-FLANN exhibits a much simpler structure, less training computation and faster convergence. The simultaneous perturbation stochastic approximation (SPSA) algorithm instead of the usual back-propagation method is applied as a learning rule of the network to adapt to the time-varying plant. Unlike the back-propagation method, the SPSA method does not require an estimation of the secondary path. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm when the ANC system exhibits nonlinear and time-varying characteristics.
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