Improvement of Clustering of ART2 Neural Network
Zhen-Ou Wang · Journal of Nanjing University of Science and Technology · 2007
In order to achieve dynamic clustering with hierarchy structure,after analyzing the shortcomings and advantages of adaptive resonance theory(ART) neural network,such as fast study,subjectively setting vigilance parameter and output without hierarchy structure;and after analyzing the shortcomings and advantages of Self-Organizing Feature Map(SOFM),such as side-feedback,inability of dynamic clustering and output without hierarchy structure,improvement of clustering algorithm of ART2 neural network has been presented with the reference of Hebb Principle.By structure description and algorithmic analysis,this model incorporates the advantages of ART2 and SOFM and overcomes their shortcomings,obtains dynamic clustering structure with multilayer hierarchy structure by fast study(each layer denotes a category of different granularity);this model also reduces 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.