RBF and CBF neural network learning procedures

We summarize our results from investigating different learning and classification algorithms for basis function limited neural networks. To achieve fast convergence we used RCE type learning procedures that have been modified for our applications and to enable simple hardware implementability. The used radial and cubic basis functions are a signum type function, a ramp function and a gaussian function. We investigated the learning algorithms to find fast and efficient procedures to automatically extract fuzzy rules and membership functions from high dimensional data which is topic of another paper.<<ETX>>