Universal approximation with Fuzzy ART and Fuzzy ARTMAP
Stephen Verzi, Gregory L. Heileman, Michael Georgiopoulos, Georgios C. Anagnostopoulos · 2004
A measure of success for any learning algorithm is how useful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically be applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we will provide a proof to show that Fuzzy ART augmented with a single layer of perceptrons is a universal approximator. Moreover, the Fuzzy ARTMAP neural network architecture, by itself, will be shown to be a universal approximator.