The issue of matching two fuzzy sets becomes an essential design aspect of many algorithms including fuzzy controllers, pattern classifiers, knowledge-based systems, etc. This paper introduces a new model of matching. Its principal features involve the following: (1) matching carried out with respect to the grades of membership of fuzzy sets as well as some functionals defined on them (like energy, entropy,transom); (2) concepts of hierarchies in the matching model leading to a straightforward distinction between 'local' and 'global' levels of matching; and (3) a distributed character of the model realized as a logic-based neural network.
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