Accessibility navigation

Mean-tracking clustering algorithm for radial basis function centre selection

Sutanto, E. L., Mason, J. D. and Warwick, K. (1997) Mean-tracking clustering algorithm for radial basis function centre selection. International Journal of Control, 67 (6). pp. 961-977. ISSN 0020-7179

Full text not archived in this repository.

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

To link to this item DOI: 10.1080/002071797223884


Radial basis functions can be combined into a network structure that has several advantages over conventional neural network solutions. However, to operate effectively the number and positions of the basis function centres must be carefully selected. Although no rigorous algorithm exists for this purpose, several heuristic methods have been suggested. In this paper a new method is proposed in which radial basis function centres are selected by the mean-tracking clustering algorithm. The mean-tracking algorithm is compared with k means clustering and it is shown that it achieves significantly better results in terms of radial basis function performance. As well as being computationally simpler, the mean-tracking algorithm in general selects better centre positions, thus providing the radial basis functions with better modelling accuracy

Item Type:Article
ID Code:17832
Publisher:Taylor & Francis

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

Page navigation