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Adaptive nonlinear controller with integrated evaluation criterion for active noise attenuation

A novel adaptive nonlinear controller is presented for nonlinear active noise control systems, which is expanded by memory function mapping on the basis of a single neuron structure, and a generalized filtered-X gradient descent algorithm is developed to attenuate the nonlinear, non-Gaussian noises, which defines the weighted sum of Renyi's quadratic error entropy and the mean square error as the integrated evaluation criterion. Parzen-window estimation method is utilized to estimate the probability density function in the proposed algorithm. In addition, the convergence of the proposed approach is analyzed. The overall scheme has a relative simple structure and less learning parameters, which can deal with nonlinear and non-Gaussian noises. The simulation results demonstrate the validity of the proposed method.