Properties of learning of a fuzzy ART variant

Author

Georgiopoulos, Michael ; Dagher, Issam ; Heileman, Gregory L. ; Bebis, George

Author_Institution

Dept. of Electr. & Comput. Eng., Central Florida Univ., Orlando, FL, USA

Volume

3

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2012

Abstract

This paper discusses one variation of the fuzzy ART architecture, referred to as fuzzy ART variant. The fuzzy ART variant is a fuzzy ART algorithm, with a very large value for the choice parameter. Based on the geometrical interpretation of templates in fuzzy ART we present and prove useful properties of learning pertaining to the fuzzy ART variant. One of these properties of learning establishes an upper bound on the number of list presentations required by the fuzzy ART variant to learn an arbitrary list of input patterns presented to it. In previously published work, it was shown that the fuzzy ART variant performs as well as a fuzzy ART algorithm with more typical values for the choice parameter. Hence, the fuzzy ART variant is as good a clustering machine as the fuzzy ART algorithm using more typical values of the choice parameter

Keywords

ART neural nets; fuzzy neural nets; geometry; learning (artificial intelligence); neural net architecture; clustering machine; fuzzy ART variant neural net architecture; geometrical interpretation; learning properties; templates; Clustering algorithms; Computer architecture; Data preprocessing; Fuzzy logic; Fuzzy neural networks; Neural networks; Pattern clustering; Subspace constraints; Supervised learning; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614209

Filename

614209