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Adaptive training of feedback neural networks for non-linear filtering

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8 Author(s)
Dreyfus, G. ; Ecole Superieure de Phys. et de Chimie Ind. de la Ville de Paris, France ; Macchi, O. ; Marcos, S. ; Nerrand, O.
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The authors propose a general framework which encompasses the training of neural networks and the adaptation of filters. It is shown that neural networks can be considered as general nonlinear filters which can be trained adaptively, i.e., which can undergo continual training. A unified view of gradient-based training algorithms for feedback networks is proposed, which gives rise to new algorithms. The use of some of these algorithms is illustrated by examples of nonlinear adaptive filtering and process identification

Published in:

Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop

Date of Conference:

31 Aug-2 Sep 1992