COMPARATIVE-ANALYSIS OF FUZZY ART AND ART-2A NETWORK CLUSTERING PERFORMANCE

Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input pa tterns into stable recognition codes. Many different types of ART-netw orks have been developed to improve clustering capabilities. In this p aper we compare clustering performance of different types of ART-netwo rks: Fuzzy ART, ART 2A with and without complement encoded input patte rns, and an Euclidean ART 2A-variation. All types are tested with two- and high-dimensional input patterns in order to illustrate general cap abilities and characteristics in different system environments. Based on our simulation results, Fuzzy ART seems to be less appropriate when ever input signals are corrupted by addititional noise, while ART 2A-t ype networks keep stable in all inspected environments. Together with other examined features, ART-architectures suited for particular appli cations can be selected.