|Table of Contents|

Improvement of Clustering of ART2 Neural Network

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2007年01期
Page:
71-75
Research Field:
Publishing date:
2007-02-28

Info

Title:
Improvement of Clustering of ART2 Neural Network
Author(s):
QIAN Xiao-dongWANG Zhen-ou
1.School of Electrical Engineering and Automation,Tianjin University, Tianjin 300072,China;2.Institute of System Engineering,Tianjin University,Tianjin 300072,China
Keywords:
adaptive resonance theory neural network clustering sel-f organizing featuremap
PACS:
TP183
DOI:
-
Abstract:
In order to achieve dynam ic clustering w ith hierarchy structure, after analyzing the shor-t com ings and advantages of adaptive resonance theory ( ART ) neural network, such as fast study, subjectively setting vigilance parameter and output w ithout hierarchy structure; and after analyzing the shortcom ings and advantages of Sel-fOrganizing FeatureMap ( SOFM ), such as side-feedback, inability of dynam ic clustering and outputw ithout hierarchy structure, improvement of clustering a-l gorithm ofART2 neural network has been presented with the reference ofH ebb Principle. By struc- ture description and algorithm ic analysis, thismodel incorporates the advantages ofART2 and SOFM and overcom es their shortcom ings, obtains dynam ic clustering structure w ith multilayer hierarchy structure by fast study( each layer denotes a category of different granularity); thismodel also re- duces the request of setting vigilance parameter and has no demand of retraining neural network of bigger granularity. Finally the effectiveness of the algorithm is demonstrated by simulation.

References:

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Memo

Memo:
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Last Update: 2007-02-28