An efficient learning method for RBF Neural Networks

Radial Basis Functions Neural Network (RBFNN) as the outcome of recent research provides a simple model for complex networks. This is achieved by employing the Radial Basis Function (RBF) in the network as hidden neuron patterns. The optimal properties of the RBFs pave the way for stable approximation. However, it is generally rather difficult to determine the locations of the centers and the shape parameter. In this article, we will present an evolutionary approach for learning parameters. The approach is based on genetic algorithms. It consists of three well-defined feed-forwarding Phases, and uses a very efficient fitness evaluation method, the so-called Power function.