A learning algorithm for radial basis function networks: with the capability of adding and pruning neurons

Radial basis function networks (RBFN) have fast learning speed because of their capability of local specialization and global generalization. By allowing the use of basis functions with different sizes (covering area), locations and orientations, RBFNs behave even more powerful and require less neurons. If an algorithm can automatically add and prune neurons, the necessary number of neurons can be further reduced. In this paper, we present such an algorithm. We select the Gaussian functions as basis functions with all the above parameters adjustable. The algorithm adds new RBFs at the places having the largest errors, and prunes neurons that have insignificant contribution. With the adding and pruning capability, it is expected that developing RBFNs for high-dimensional problems will become more feasible