How do MLPs compare with RBFs?
Multilayer perceptrons (MLPs) and radial basis function (RBF) networks are the two most commonly-used types of feedforward network. They have much more in common than most of the NN literature would suggest. The only fundamental difference is the way in which hidden units combine values coming from preceding layers in the network–MLPs use inner products, while RBFs use Euclidean distance. There are also differences in the customary methods for training MLPs and RBF networks, although most methods for training MLPs can also be applied to RBF networks. Furthermore, there are crucial differences between two broad types of RBF network–ordinary RBF networks and normalized RBF networks–that are ignored in most of the NN literature. These differences have important consequences for the generalization ability of the networks, especially when the number of inputs is large.
