Investigating the synthesis of RBF networks

The approximation capability of RBF networks is investigated using a test function and a fixed finite number of training data. The test function used allows to confirm the recently introduced concept of second derivative dependent placement of RBF centers. Different Gaussian RBF networks are trained varying the width and the number of centers (number of hidden units). The dependence of the approximation error on these network parameters is studied experimentally.