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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Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 18–28, November–December 2006.
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Bodyanskiy, Y.V., Teslenko, N.A. Adaptive learning of fuzzy BSB and GBSB neural models. Cybern Syst Anal 42, 786–794 (2006). https://doi.org/10.1007/s10559-006-0119-y
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DOI: https://doi.org/10.1007/s10559-006-0119-y