System identification
Structure identification of nonlinear dynamic systems—a survey on input/output approaches
Automatica (Journal of IFAC)
IEEE Spectrum
Active Noise Control Systems: Algorithms and DSP Implementations
Active Noise Control Systems: Algorithms and DSP Implementations
Active control of nonlinear noise processes in a linear duct
IEEE Transactions on Signal Processing
Adaptive Volterra filters for active control of nonlinear noiseprocesses
IEEE Transactions on Signal Processing
Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes
IEEE Transactions on Audio, Speech, and Language Processing
An alternative solution to the model structure selection problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improved training of neural networks for the nonlinear active control of sound and vibration
IEEE Transactions on Neural Networks
Active control of vibration using a neural network
IEEE Transactions on Neural Networks
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The extension of active noise control (ANC) techniques to deal with nonlinear effects such as distortion and saturation requires the introduction of suitable nonlinear model classes and adaptive algorithms. Large sized models are typically used, resulting in an increased computational load, delayed convergence (and sometimes even algorithm instability), and other unwanted dynamical effects due to overparametrization. This paper discusses the usage of polynomial Nonlinear AutoRegressive models with eXogenous variables (NARX) models and model selection techniques to reduce the model size and increase its robustness, for more efficient and reliable ANC. An offline procedure is devised to identify the controller model structure, and the controller parameters are successively updated with an adaptive algorithm based on the error gradient and on the residual noise. Simulation experiments show the effectiveness of the proposed approach. A brief analysis of the involved computational complexity is also provided.