Design of active noise control system using hybrid functional link artificial neural network and finite impulse response filters

The active noise control is the best approach to limit the low-frequency noise present in any applications and also to estimate the signals, which are corrupted by interference or additive noise. In this paper, the design of an active noise control system is proposed with the hybrid combination of functional link artificial neural network and finite impulse response filter. The filter coefficients of both functional link artificial neural network and finite impulse response filters are optimized by firefly algorithm, and the ensemble mean square error value is computed through the filtered-s least mean square algorithm, in which the convergence of the filter is improved through the proposed approach. The signals like Chaotic noise signal and Gaussian noise signal are considered to assess the proposed method. Then, the noise reduction capability of the proposed active noise control with firefly algorithm is compared with that achieved by the same active noise control with another optimization algorithm known as BAT algorithm in place of firefly algorithm. The performance analysis is carried out under the MATLAB environment.