Properties of learning of a Fuzzy ART Variant

被引：23

作者：

Georgiopoulos, M ^{[1
]}

Dagher, I

Heileman, GL

Bebis, G

机构：

[1] Univ Cent Florida, Dept Elect & Comp Engn, Orlando, FL 32816 USA

[2] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA

[3] Univ Nevada, Dept Comp Sci, Reno, NV 89557 USA

来源：

NEURAL NETWORKS | 1999年 / 12卷 / 06期

关键词：

neural network; unsupervised learning; supervised learning; clustering; adaptive resonance theory;

D O I：

10.1016/S0893-6080(99)00031-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 uf 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. (C) 1999 Elsevier Science Ltd. All rights reserved.

引用

页码：837 / 850

页数：14

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