Abstract
A fuzzy ART neural network model based on the closeness theory, called CBFART, is introduced in this paper. It incorporates two concepts of the fuzzy set theory, the closeness and closet principle with the adaptive resonance theory (ART), to form a new neural network model. The model is characterized with a matching-consigning cycle, and classification of patterns in the network is followed by the closest principle. The complement coding, matching consigning, and fast learning-slow re-coding procedure work together to make sure that the learning of the network is converging and stable. The above three elements also make one shot learning to be practicable, so as to improve the learning speed of the network. The concrete algorithm of the model and the result of simulation are given, and an analysis shows that the model has a well clustering performance.
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