UNIFIED FRAMEWORK FOR MLPs AND RBFNs: INTRODUCING CONIC SECTION FUNCTION NETWORKS

Multilayer perceptions (MLPs) (Werbos, 1974; Rumelhart et al., 1986) and radial basis function networks (RBFNs) (Broomhead and Lowe, 1988; Moody and Darken, 1989) are probably the most widely used neural network models for practical applications. Whereas 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 and Lowe, 1988). On first sight, the two

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