Universal Approximation With Fuzzy Art And Fuzzy Artmap
Adaptive Resonance Theory; Machine Learning; Neural Networks; Universal Function Approximation
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.
Proceedings of the International Joint Conference on Neural Networks
Number of Pages
Article; Proceedings Paper
Source API URL
Verzi, Stephen J.; Heileman, Gregory L.; and Georgiopoulos, Michael, "Universal Approximation With Fuzzy Art And Fuzzy Artmap" (2003). Scopus Export 2000s. 1590.