Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Active Noise Control Systems: Algorithms and DSP Implementations
Active Noise Control Systems: Algorithms and DSP Implementations
A real-time learning algorithm for a multilayered neural networkbased on the extended Kalman filter
IEEE Transactions on Signal Processing
Improved training of neural networks for the nonlinear active control of sound and vibration
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Active control of vibration using a neural network
IEEE Transactions on Neural Networks
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In this paper, active noise control using recurrent neural networks is addressed. A new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter is developed for active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network is used to model secondary path of active noise control while the second one is employed to generate control signal. Real-time experiment of the proposed algorithm using digital signal processor is carried-out to show the effectiveness of the method.