An optimal full-genetic technique used to train RBF neural networks

It is well-known that, the pattern recognition performances assigned to RBF neural networks depends a lot by their specific training algorithms, and by the methods used for RBF center selection (e.g., a clustering technique), particularly. Having as starting point the membership of genetic algorithms to the powerful class of global optimization methods, an optimal full-genetic training procedure of RBF neural networks based on hybrid genetic clustering algorithm used for center mapping, and on genetic approach to fit the output neural weights is proposed. Finally, using a real pattern recognition task, a comparative study (as performance level) with others standard RBF training methods and SART neural network is also described.