Radial basis function networks in A+

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.