ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
EURASIP Journal on Applied Signal Processing
Filtered-X affine projection algorithms for active noise control using Volterra filters
EURASIP Journal on Applied Signal Processing
Nonlinear active noise control using EKF-based recurrent fuzzy neural networks
International Journal of Hybrid Intelligent Systems
Adjoint EKF learning in recurrent neural networks for nonlinear active noise control
Applied Soft Computing
Filtered-X Adaptive Neuro-Fuzzy Inference Systems for Nonlinear Active Noise Control
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
Nonlinear active noise control using NARX model structure selection
ACC'09 Proceedings of the 2009 conference on American Control Conference
Nonlinear active noise control with NARX models
IEEE Transactions on Audio, Speech, and Language Processing
Enhanced neural filter design and its application to the active control of nonlinear noise
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Nonlinear feedback active noise control for broadband chaotic noise
Applied Soft Computing
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Active control of sound and vibration has been the subject of a lot of research, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed and/or lower computational loads. Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers