Extended Kalman Filter Trained Recurrent Radial Basis Function Network in Nonlinear System Identification

Authors:
Branimir Todorovic;Miomir Stankovic;Claudio Moraga
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 1
Cited 1

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Abstract

We consider the recurrent radial basis function network as a model of nonlinear dynamic system. On-line parameter and structure adaptation is unified under the framework of extended Kalman filter. The ability of adaptive system to deal with high observation noise, and the generalization ability of the resulting RRBF network are demonstrated in nonlinear system identification.