Fuzzy ART properties

Juxin Huang; Michael Georgiopoulos; Gregory L. Heileman

doi:10.1016/0893-6080(94)00073-U

Fuzzy ART properties

Juxin Huang, Michael Georgiopoulos, Gregory L. Heileman

Research output: Contribution to journal › Article › peer-review

71 Scopus citations

Abstract

This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. 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 effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.

Original language	English (US)
Pages (from-to)	203-213
Number of pages	11
Journal	Neural Networks
Volume	8
Issue number	2
DOIs	https://doi.org/10.1016/0893-6080(94)00073-U
State	Published - 1995
Externally published	Yes

Keywords

Adaptive resonance theory
Clustering
Fuzzy ART
Fuzzy set theory
Learning
Neural network
Pattern recognition

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

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10.1016/0893-6080(94)00073-U

Cite this

@article{f459a72bcd0c4980a37a99235a8b9fd7,

title = "Fuzzy ART properties",

abstract = "This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. 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 effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.",

keywords = "Adaptive resonance theory, Clustering, Fuzzy ART, Fuzzy set theory, Learning, Neural network, Pattern recognition",

author = "Juxin Huang and Michael Georgiopoulos and Heileman, {Gregory L.}",

note = "Funding Information: In this paper we present useful properties of the Fuzzy ART algorithm that facilitate the understanding of its operation. For clarity purposes we split the properties into four different categories: template properties (Section 3 ), access properties (Section 4), reset properties (Section 5 ), and properties related to the number of list presentations needed for the weight stabilization Acknowledgements: This research was supported in part by a grant from the Florida High Technology and Industry Council, in part by a grant from the Division of Sponsored Research at the University of Central Florida, and in part by a grant from Boeing Computer Services under contract W-300445.",

year = "1995",

doi = "10.1016/0893-6080(94)00073-U",

language = "English (US)",

volume = "8",

pages = "203--213",

journal = "Neural Networks",

issn = "0893-6080",

publisher = "Elsevier Ltd",

number = "2",

}

TY - JOUR

T1 - Fuzzy ART properties

AU - Huang, Juxin

AU - Georgiopoulos, Michael

AU - Heileman, Gregory L.

N1 - Funding Information: In this paper we present useful properties of the Fuzzy ART algorithm that facilitate the understanding of its operation. For clarity purposes we split the properties into four different categories: template properties (Section 3 ), access properties (Section 4), reset properties (Section 5 ), and properties related to the number of list presentations needed for the weight stabilization Acknowledgements: This research was supported in part by a grant from the Florida High Technology and Industry Council, in part by a grant from the Division of Sponsored Research at the University of Central Florida, and in part by a grant from Boeing Computer Services under contract W-300445.

PY - 1995

Y1 - 1995

N2 - This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. 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 effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.

AB - This paper presents some important properties of the Fuzzy ART neural network algorithm introduced by Carpenter, Grossberg, and Rosen. 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 effects of the Fuzzy ART parameters a and p on the functionality of the algorithm are clearly illustrated.

KW - Adaptive resonance theory

KW - Clustering

KW - Fuzzy ART

KW - Fuzzy set theory

KW - Learning

KW - Neural network

KW - Pattern recognition

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DO - 10.1016/0893-6080(94)00073-U

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SN - 0893-6080

VL - 8

SP - 203

EP - 213

JO - Neural Networks

JF - Neural Networks

IS - 2

ER -

Fuzzy ART properties

Abstract

Keywords

ASJC Scopus subject areas

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