Radial basis function networks in A+

Authors:
Alexander Skomorokhov
Affiliations:
Institute of Nuclear Power Engineering, Kaluga Region, 249020, Russia
Venue:
APL '02 Proceedings of the 2002 conference on APL: array processing languages: lore, problems, and applications
Year:
2002

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Abstract

This paper discusses an implementation and application of Radial Basis Function (RBF) Networks. This type of neural networks performs a universal approach to function approximation. The same algorithm and program may be successfully applied to regression modeling or pattern classification. We illustrate the most important characteristics of RBF networks with a number of examples and discuss network behavior in depth. The software has been implemented in the A+ language, which became available to developers in January of 2001.