Authors: Todorović, Branimir
Stanković, Miomir 
Moraga, Claudio
Title: Modeling non-stationary dynamic system using recurrent radial basis function networks
Journal: 2002 6th Seminar on Neural Network Applications in Electrical Engineering, NEUREL 2002 - Proceedings
First page: 27
Last page: 32
Issue Date: 1-Jan-2002
ISBN: 0-7803-7593-9
DOI: 10.1109/NEUREL.2002.1057961
This paper addresses the problem of continuous adaptation of neural networks in a non-stationary environment. We have applied the extended Kalman filter to the parameter, state and structure estimation of a recurrent radial basis function network. The architecture of the recurrent radial basis function network implements a nonlinear autoregressive model with exogenous inputs. Statistical criteria for structure adaptation (growing and pruning of hidden units and connections of the network) were derived using statistics estimated by the Kalman filter. The proposed algorithm is applied to non-stationary dynamic system modeling.
Keywords: extended Kalman filter | nework growing | non-stationary | on-line learning | pruning | recurrent RBF | structure adaptation
Publisher: IEEE

Show full item record


checked on May 23, 2024

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.