Universal Approximation with Fuzzy ART and Fuzzy ARTMAP

Stephen J. Verzi, Gregory L. Heileman, Michael Georgiopoulos, Georgios C. Anagnostopoulos

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Number of pages6
StatePublished - 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003


ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR


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

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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