Properties of learning in fuzzy ART

Juxin Huang, Michael Georgiopoulos, Gregory L. Heileman

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

This paper presents some important properties of the Fuzzy ART neural network algorithm. 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 effect of the Fuzzy ART parameters α and ρ on the functionality of the algorithm is clearly illustrated.

Original languageEnglish (US)
Pages756-761
Number of pages6
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

ASJC Scopus subject areas

  • Software

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