Abstract
This paper is concerned with radial basis function neural networks. A new method is described by which means radial basis function centres can be selected. The method is based on a mean tracking clustering algorithm and it is shown how the approach provides a good procedure for radial basis function centre selection. Results are given which indicate how the method realises a network with excellent approximation properties.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Girosi, F., Poggio, T.: Biol. Cybernetics, 63, 169, (1990).
Cichocki, A., Unbehauen, R.: Neural Networks for Optimization and Signal Processing. John Wiley and Sons, New York 1993.
Chen, S., Billings, S.A., Cowan, C.F., Grant, P.M.: Int. Journal of Control, 52, 1327 (1990).
Narendra, K.S.: Adaptive control of Dynamica Systems using Neural Networks, in Handbook of Intelligent Control, van Nostrand 1994.
Sutanto, E., Warwick, K.: Proc. Cybernetics and Systems, 1, ed. Robert Trappl, 327 (1994).
Craddock, R.J., Mason, J.D., Warwick, K: Proc IMACS Int. Sympòsium on Signal Processing Robotics and Neural Networks, 302 (1994).
Soderstrom, T., Stoica, P.: System Identification. Prentice-Hall (1989).
Vogt, W., Nagel, D.: Clinical Chemistry, 38, (1992).
Huth, R., Nemesova, I., Klimperova, N.: International Journal of Climatology, l3, 817 (1993)
Sutanto, E.L.: The Design and Development of Multivariable Cluster Analysis for High-Speed Machinery. PhD Thesis, University of Reading (1995).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag/Wien
About this paper
Cite this paper
Warwick, K., Mason, J.D., Sutanto, E.L. (1995). Centre Selection for Radial Basis Function Networks. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_81
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7535-4_81
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive