ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
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
A Nonlinear ANC System with a SPSA-Based Recurrent Fuzzy Neural Network Controller
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
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
Model-Free control of a nonlinear ANC system with a SPSA-Based neural network controller
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Nonlinear feedback active noise control for broadband chaotic noise
Applied Soft Computing
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Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems