Using radial basis function networks for function approximation

tasks suffers from unavailable knowledge about an adequate network

size. In this work, a measuring technique is proposed which can control the

model complexity and is based on the correlation coefficient between two

basis functions. Simulation results show good performance and, therefore,

this technique can be integrated in the RBF training procedure.