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Denoise based Algorithm for Active Noise Control Systems using Genetic Algorithm

A literature survey suggests that the filtered-x least mean rectangular (FxLMS) algorithm is the most famous adaptive algorithm for ANC systems, due particularly to its simplicity and strong overall performance. However, the convergence speed of the FxLMS algorithm is gradual. Furthermore, its overall performance is degraded while there's a large measurement noise inside the reference and error signals. To take care of these issues, this paper proposes a proposes an adaptive filtering with denoise based algorithm for ANC systems. This algorithm utilizes a comparative structure as that of the FxLMS algorithm based ANC systems. On this paper two new strategies have been proposed to accomplish enhanced execution in ANC structures. The primary technique proposed utilizes wavelet denoise idea as a part of the secondary path. The estimated output is a critical aspect inside the calculations of threshold value, considering the fact that the threshold value is used in the calculations of step size. As the error increases, threshold decreases and step size will increase and vice versa. For in addition development in performance, the genetic set of rules is used. Here the fitness function is described, which tunes the threshold value so one can reduce the error significantly.