Neural network basis function center selection using cluster analysis

Full text not archived in this repository.

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Warwick, K., Mason, J. D. and Sutanto, E. L. (1995) Neural network basis function center selection using cluster analysis. In: Proceedings of the American Control Conference 1995. IEEE, pp. 3780-3781. ISBN 0780324455 doi: 10.1109/ACC.1995.533845

Abstract/Summary

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Altmetric Badge

Item Type Book or Report Section
URI https://reading-clone.eprints-hosting.org/id/eprint/21668
Identification Number/DOI 10.1109/ACC.1995.533845
Refereed Yes
Divisions Science
Uncontrolled Keywords basis function center selection, cluster analysis, convergence, function modelling, mean-tracking clustering, radial basis function networks
Publisher IEEE
Download/View statistics View download statistics for this item

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar