Design of dynamic linear-in-the-parameters nonlinear filters for active noise control

Traditional active noise control (ANC) systems, which uses a fixed tap length adaptive filter as the controller may lead to non optimal noise mitigation. In addition, the conventional filtered-x least mean square algorithm based ANC schemes fail to effectively perform noise cancellation in the presence of nonlinearities in the ANC environment. In order to overcome these limitations of traditional ANC techniques, in this paper, we propose a class of dynamic nonlinear ANC systems, which adapts itself to the noise cancellation scenario. The dynamic behaviour has been achieved by developing a variable tap length and variable learning rate adaptive algorithms for functional link artificial neural network (FLANN) and generalized FLANN (GFLANN) based ANC systems. The proposed ANC schemes have been shown through a simulation study to provide an optimal convergence behaviour. This improvement has been achieved by providing a balance between the number of filter coefficients and the mean square error.