Title

Properties Of Learning Of A Fuzzy Art Variant

Keywords

Adaptive resonance theory; Clustering; Neural network; Supervised learning; Unsupervised learning

Abstract

This paper discusses a variation of the Fuzzy ART algorithm referred to as the Fuzzy ART Variant. The Fuzzy ART Variant is a Fuzzy ART algorithm that uses a very large choice parameter value. Based on the geometrical interpretation of the weights in Fuzzy ART, useful properties of learning associated with the Fuzzy ART Variant are presented and proven. One of these properties establishes an upper bound on the number of list presentations required by the Fuzzy ART Variant to learn an arbitrary list of input patterns. This bound is small and demonstrates the short-training time property of the Fuzzy ART Variant. Through simulation, it is shown that the Fuzzy ART Variant is as good a clustering algorithm as a Fuzzy ART algorithm that uses typical (i.e. small) values for the choice parameter. Copyright (C) 1999 Elsevier Science Ltd.

Publication Date

7-1-1999

Publication Title

Neural Networks

Volume

12

Issue

6

Number of Pages

837-850

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/S0893-6080(99)00031-3

Socpus ID

0032797396 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0032797396

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