Learning methods for radial basis function networks

Authors:
Roman Neruda;Petra Kudová
Affiliations:
Institute of Computer Science, Academy of Sciences of the Czech Republic, P.O. Box 5, 18207 Prague, Czech Republic;Institute of Computer Science, Academy of Sciences of the Czech Republic, P.O. Box 5, 18207 Prague, Czech Republic
Venue:
Future Generation Computer Systems
Year:
2005

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Abstract

RBF networks represent a vital alternative to the widely used multilayer perceptron neural networks. In this paper we present and examine several learning methods for RBF networks and their combinations. A gradient-based learning, the three-step algorithm with unsupervised part, and an evolutionary algorithms are introduced, and their performance compared on benchmark problems from the Proben1 database. The results show that the three-step learning is usually the fastest, while the gradient learning achieves better precision. The best results can be achieved by employing hybrid approaches that combine presented methods.