Adjoint EKF learning in recurrent neural networks for nonlinear active noise control

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 recu...

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Bibliographic Details
Published in:Applied soft computing 2008-09, Vol.8 (4), p.1498-1504
Main Author: Bambang, Riyanto T.
Format: Article
Language:English
Subjects:
Active noise control
Adjoint learning algorithm
DSP
Extended Kalman Filter
Nonlinearity
Real-time experiment
Recurrent neural networks
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Summary: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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2007.10.017