Fuzzy ART properties

Juxin Huang, Michael Georgiopoulos, Gregory L. Heileman

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. The properties described in the paper are distinguished into a number of categories. These include template, access, and reset properties, as well as properties related to the number of list presentations needed for weight stabilization. These properties provide numerous insights as to how Fuzzy ART operates. Furthermore, the effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.

Original languageEnglish (US)
Pages (from-to)203-213
Number of pages11
JournalNeural Networks
Issue number2
StatePublished - 1995
Externally publishedYes


  • Adaptive resonance theory
  • Clustering
  • Fuzzy ART
  • Fuzzy set theory
  • Learning
  • Neural network
  • Pattern recognition

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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