A Fuzzy Clustering Algorithm to Estimate the Parameters of Radial Basis Functions Neural Networks and Its Application to System Modeling

Authors:
Antonios D. Niros;George E. Tsekouras
Affiliations:
Department of Cultural Technology and Communication, University of the Aegean, Mytilini, Greece 81100;Department of Cultural Technology and Communication, University of the Aegean, Mytilini, Greece 81100
Venue:
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Year:
2008

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Abstract

We propose a two-staged fuzzy clustering algorithm to train radial basis function neural networks. The novelty of the contribution lies in the way we handle the input training data information between the two stages of the algorithm. The back-propagation method is employed to optimize the network parameters. The number of hidden nodes is determined by the iterative implementation of the fuzzy clustering and the back-propagation. Simulation results show that the methodology produces accurate models compared to the standard and more sophisticated techniques reported in the literature.