Abstract
Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART-networks have been developed to improve clustering capabilities. In this paper we compare clustering performance of different types of ART-networks: Fuzzy ART, ART 2A with and without complement encoded input patterns, and an Euclidean ART 2A-variation. All types are tested with two- and high-dimensional input patterns in order to illustrate general capabilities and characteristics in different system environments. Based on our simulation results, Fuzzy ART seems to be less appropriate whenever input signals are corrupted by additional noise, while ART 2A-type networks keep stable in all inspected environments. Together with other examined features, ART-architectures suited for particular applications can be selected.
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