|Table of Contents|

Improvement of Clustering of ART2 Neural Network


Research Field:
Publishing date:


Improvement of Clustering of ART2 Neural Network
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
adaptive resonance theory neural network clustering sel-f organizing featuremap
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.


[ 1] KantardzicM. Data m ining concepts, models, meth- ods and algorithms [M ]. 闪四清, 陈茵, 程娃, 译. 北京: 清华大学出版社, 2003.
[ 2] H aganM T, Demuth H B. 神经网络设计 [M ]. 戴葵, 译. 北京: 机械工业出版社, 2002.
[ 3] 王莉, 王正欧. TGSOM: 一种用于数据聚类的动态自组织映射神经网络 [ J]. 电子与信息学报, 2003, 25( 3): 313- 319.
[ 4] A lahakoon D, Halgamuge S K, Srinivasan B. Dynam- ic sel-f organizing maps w ith controlled growth for know ledge discovery [ J]. NeuralNetworks, 2000, 11 ( 3): 601- 614.
[ 5] Abhijit S P, RobertBM. 神经网络模式识别及其实现 [M ]. 徐 勇, 荆 涛, 译. 北京: 电子 工业 出版社, 1999.
[ 6] DavenportM P, T itusA. M ultilevelcategory structure in the ART2 network [ J]. Neural Networks, 2004, 15 ( 1): 145- 158.


Last Update: 2007-02-28