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NeuroCOLT
Technical Report NC-TR-96-038
Active Noise Control
with Dynamic Recurrent Neural Networks
Davor
Pavisic
Faculté Polytechnique de Mons
Belgium
Laurent
Blondel
Faculté Polytechnique de Mons
Belgium
Jean-Philipe
Draye
Faculté Polytechnique de Mons
Belgium
Gaëtan
Libert
Faculté Polytechnique de Mons
Belgium
Pierre
Chapelle
Faculté Polytechnique de Mons
Belgium
Abstract
We have developed a neural active noise controller which performs
better than existing techniques. We used a dynamic recurrent neural
network to model the behaviour of an existing controller that uses
a Least Mean Squares algorithm to minimize an error signal. The network
has two types of adaptive parameters, the weights between the units
and the time constants associated with each neuron. Measured results
show a significant improvement of the neural controller when compared
with the existing system.
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