FUZZY REPRESENTATION AND INFERENCE METHODS

Two essential components of systems modeling are i) representation and ii) inference. We review recent developments in fuzzy systems modeling from a perspective of: a) knowledge representation with fuzzy sets including measurement and acquisition of membership functions for a system parameter identification, as well as combination of knowledge with fuzzy sets for the formation of rules in a system structure identification, and b) approximate reasoning with fuzzy logic including properties of reasoning, combination of rules and/or their consequences, and three heuristics that have been proposed during the course of development. This review is restricted to point-valued fuzzy sets and logics.