N2 - The development of digital signal processors and the increase in their computing capabilities bring opportunities to employ algorithms with multiple variable parameters in active noise control systems. Of particular interest are the algorithms based on artificial neural networks. This paper presents an active noise control algorithm based on a neural network and a nonlinear input-output system identification model. The purpose of the algorithm is an active noise control system with a nonlinear primary path. The algorithm uses the NARMAX system identification model. The neural network employed in the proposed algorithm is a multilayer perceptron. The error backpropagation rule with adaptive learning rate is employed to update the weight of the neural network. The performance of the proposed algorithm has been tested by numerical simulations. Results for narrow-band input signals and nonlinear primary path are presented below. L1 - http://www.journals.pan.pl/Content/107531/PDF-MASTER/75.pdf L2 - http://www.journals.pan.pl/Content/107531 PY - 2010 IS - No 2 EP - 202 DO - 10.2478/v10168-010-0018-0 KW - active noise control KW - neural networks KW - system identification KW - nonlinear phenomena A1 - Krukowicz, Tomasz PB - Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics VL - vol. 35 DA - 2010 T1 - Active Noise Control Algorithm Based on a Neural Network and Nonlinear Input-Output System Identification Model SP - 191 UR - http://www.journals.pan.pl/dlibra/publication/edition/107531 T2 - Archives of Acoustics