Title

Universal Approximation With Fuzzy Art And Fuzzy Artmap

Keywords

Adaptive Resonance Theory; Machine Learning; Neural Networks; Universal Function Approximation

Abstract

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.

Publication Date

9-24-2003

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Volume

3

Number of Pages

1987-1992

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

0141682847 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0141682847

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