Title :
Adaptive training of feedback neural networks for non-linear filtering
Author :
Dreyfus, G. ; Macchi, O. ; Marcos, S. ; Nerrand, O. ; Personnaz, L. ; Roussel-Ragot, P. ; Urbani, D. ; Vignat, C.
Author_Institution :
Ecole Superieure de Phys. et de Chimie Ind. de la Ville de Paris, France
fDate :
31 Aug-2 Sep 1992
Abstract :
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
Keywords :
adaptive filters; learning (artificial intelligence); recurrent neural nets; adaptive filtering; adaptive training; feedback neural networks; gradient-based training algorithms; nonlinear filters; process identification; Adaptive control; Adaptive filters; Feedforward neural networks; Filtering algorithms; Industrial training; Neural networks; Neurofeedback; Neurons; Output feedback; Programmable control;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253657