An overview of radial basis function networks

This chapter presents a broad overview of Radial Basis Function Networks (RBFNs), and facilitates an understanding of their properties by using concepts from approximation theory, catastrophy theory and statistical pattern recognition. While this chapter is aimed to provide an adequate theoretical background for the subsequent application oriented chapters in this book, it also covers several aspects with immediate practical implications: alternative ways of training RBFNs, how to obtain an appropriate network size for a given problem, and the impact of the resolution (width) of the radial basis functions on the solution obtained. Some prominent applications of RBFNs are also outlined.

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