Adjoint EKF learning in recurrent neural networks for nonlinear active noise control |
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Authors: | Riyanto T Bambang |
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Affiliation: | aSchool of Electrical Engineering and Informatics, Institute Technology of Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia |
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Abstract: | 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. |
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Keywords: | Recurrent neural networks Extended Kalman Filter Adjoint learning algorithm Active noise control Nonlinearity Real-time experiment DSP |
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