DocumentCode
353987
Title
Closeness theory based fuzzy ART model
Author
Dianzhi, Zhang ; Wenhua, Liu ; Hongli, Lei
Author_Institution
Air Force Eng. Coll. of China, Xi´´an, China
Volume
3
fYear
2000
fDate
2000
Firstpage
1628
Abstract
A fuzzy ART neural network model based on the closeness theory, called CBFART, is introduced in this paper. It incorporates two concepts of the fuzzy set theory, the closeness and closet principle with the adaptive resonance theory (ART), to form a new neural network model. The model is characterized with a matching-consigning cycle, and classification of patterns in the network is followed by the closest principle. The complement coding, matching consigning, and fast learning-slow re-coding procedure work together to make sure that the learning of the network is converging and stable. The above three elements also make one shot learning to be practicable, so as to improve the learning speed of the network. The concrete algorithm of the model and the result of simulation are given, and an analysis shows that the model has a well clustering performance
Keywords
ART neural nets; encoding; fuzzy neural nets; learning (artificial intelligence); pattern classification; ART neural network; adaptive resonance theory; closeness theory; fuzzy ART model; fuzzy neural network; learning; matching-consigning cycle; pattern classification; Analytical models; Clustering algorithms; Concrete; Fuzzy neural networks; Fuzzy set theory; Neural networks; Pattern matching; Performance analysis; Resonance; Subspace constraints;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on
Conference_Location
Hefei
Print_ISBN
0-7803-5995-X
Type
conf
DOI
10.1109/WCICA.2000.862744
Filename
862744
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