Controlling complexity of RBF networks by similarity

Abstract. 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.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Ulrich Rückert, Ralf Eickhoff
• Keywords: Basis Functions, ESANN 2007, Function Approximation Tasks, Neural Networks, Radial Basis Function