Fuzzy neural networks and neurocomputations

Abstract The paper discusses relationships between conceptual and computational platforms of fuzzy sets and neurocomputations. Fuzzy sets provide an interesting and powerful scheme of knowledge representation with its clear-cut logical properties. Neural networks with their superb learning capabilities play a primordial role in numerical processing. The synergy between these two paradigms will be studied. Two new classes of basic logic-oriented processing units (fuzzy neurons) will be introduced. The first one embraces aggregative neurons realizing OR, AND and mixed OR/AND operations. The neurons of the reference character constituting the second category of the processing units pertain to a referential character of processing that includes manipulation on binary relations of matching, difference, inclusion and dominance. The proposed architecture of logic processors implements the paradigm of distributed processing with the aid of logic-driven neurons. Various application domains (fuzzy controllers, machine learning, decision-making and distributed modelling) will be studied.

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