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Design of radial basis function networks using decision trees

A radial basis function (RBF) neural network is considered as one of the universal input-output functional mapping learning systems. Important issues in designing an efficient RBF neural network are the number of neurons and the shape and location of neurons to define local receptive fields in feature space. This paper presents a solution to these problems using decision tree partitioning. A conversion algorithm from decision tree to RBF neural network is described. Two examples are presented to illustrate the proposed approach.