Introduction of the Radial Basis Function (RBF) Networks
نویسنده
چکیده
In this paper we provide a short overview of the Radial Basis Functions (RBF), their properties, the motivations behind their use and some of their applications. RBF’s have been employed for functional approximation in time-series modeling and in pattern classification. They have been shown to implement the Bayesian rule and to model any continuous inputoutput mapping. RBF’s are embedded in a two-layer neural network topology. We present the physical and statistical significance of the elements composing the network. We introduce a few RBF training algorithms and we show how RBF networks can be used in real applications.
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