Using unscented kalman filter for training the minimal resource allocation neural network

Authors:
Ye Zhang;Yiqiang Wu;Wenquan Zhang;Yi Zheng
Affiliations:
Electronic Information & Engineering Faculty, Nanchang University, Nanchang, China;Electronic Information & Engineering Faculty, Nanchang University, Nanchang, China;Electronic Information & Engineering Faculty, Nanchang University, Nanchang, China;Computers information engineering college & Engineering Faculty, Jiangxi normal university, Nanchang, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

The MARN has the same structure as the RBF network and has the ability to grow and prune the hidden neurons to realize a minimal network structure. Several algorithms have been used to training the network. This paper proposes the use of Unscented Kalman Filter (UKF) for training the MRAN parameters i.e. centers, radii and weights of all the hidden neurons. In our simulation, we implemented the MRAN trained with UKF and the MRAN trained with EKF for states estimation. It is shown that the MRAN trained with UKF is superior than the MRAN trained with EKF.