Properties of learning of a Fuzzy ART Variant

    Authors

    M. Georgiopoulos; I. Dagher; G. L. Heileman;G. Bebis

    Comments

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    Abbreviated Journal Title

    Neural Netw.

    Keywords

    neural network; unsupervised learning; supervised learning; clustering; adaptive resonance theory; NEURAL-NETWORK; ARCHITECTURE; RECOGNITION; CLASSIFICATION; PATTERNS; SEARCH; Computer Science, Artificial Intelligence

    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 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.

    Journal Title

    Neural Networks

    Volume

    12

    Issue/Number

    6

    Publication Date

    1-1-1999

    Document Type

    Article

    Language

    English

    First Page

    837

    Last Page

    850

    WOS Identifier

    WOS:000082104300005

    ISSN

    0893-6080

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