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Języki publikacji
Abstrakty
In this paper, the active noise control system based on an adaptively trained recurrent neural network (RNN) is proposed. In our research, we assume that secondary path is modelled by nonlinear transfer function, i.e. linear filter followed by a saturation nonlinearity. To compare commonly used filtered-X LMS (FXLMS) and filtered-X RLS (FXRLS) algorithms with the proposed RNN-based scheme, computer simulations are carried out. Results of experiments have shown that the adaptively trained recurrent neural network provides a significant improvement in an operation of the adaptive ANC systems in the steady-state in comparison with classically used FXLMS and FXRLS algorithms.
Wydawca
Rocznik
Tom
Strony
263--270
Opis fizyczny
Bibliogr. 4 poz., rys.
Twórcy
autor
- Institute of Electronic Systems, Warsaw University of Technology, Poland
autor
- Institute of Electronic Systems, Warsaw University of Technology, Poland
autor
- Institute of Radioelectronics, Warsaw Univeristy of Technology, Poland
Bibliografia
- [1] Costa M.H., Bermudez J.C., Bershad N.J.: Stochastic Analysis of the Filtered-X LMS Algorithm in Systems with Nonlinear Secondary Path. IEEE Transaction on Signal Processing, 50, 6,2002,1327-1342
- [2] Kuo S.M., Morgan D.R.: Active Noise Control: A Tutorial Review. Proceedings of the IEEE, 87, 6,1999,943-973
- [3] Nerrand O., Roussel-Ragot P., Personnaz L., Dreyfus G.: Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms. Neural Computations MIT, 5,1993,165-199
- [4] Haykin S.: Adaptive Filter Theory. New Jersey, Prentice Hall 1996
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ4-0002-0032