Abstract
This article deals with a special class of neural autoassociative memory, namely, with fuzzy BSB and GBSB models and their learning algorithms. These models defined on a hypercube solve the problem of fuzzy clusterization of a data array owing to the fact that the vertices of the hypercube act as point attractors. A membership function is introduced that allows one to classify data that belong to overlapping clusters.
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