A Unified Framework for MLPs and RBFNs: Introducing Conic Section Function Networks

Multilayer Perceptrons (MLP, Werbos 1974, Rumelhart et al. 1986) and Radial Basis Function Networks (RBFN, Broomhead & Lowe 1988, Moody & Darken 1989) probably are the most widely used neural network models for practical applications. While the former belong to a group of "classical " neural networks (whose weighted sums are loosely inspired by biology), the latter have risen only recently from an analogy to regression theory (Broomhead & Lowe 1988). On first sight, the two models---except for being multilayer feedforward networks---do not seem to have much in common. On second thought, however, MLPs and RBFNs share a variety of features, worthy of viewing them in the same context and comparing them to each other with respect to their properties. Consequently, a few attempts on arriving at a unified picture of a class of feedforward networks--- with MLPs and RBFNs as members---have been undertaken (Robinson et al. 1988, Maruyama et al. 1992, Dorffner 1992, 1993). Most of these attempts..