A COMPARISON OF DIFFERENT CLASSES FOR FUZZY IMPLICATION IN MODELING AN APPROXIMATE REASONING MODEL

Abstract

Nearly after a decade after the idea given by Zadeh (1965), the theory of fuzzy sets started to provide a powerful and useful tool for the modeling of "daily life systems" which involve decision processes. The most important operator which plays a very important role for decision making in intelligent systems is the implication operator. In this paper, we study the different models of fuzzy operations of Min., Max. and Inverse, given by various researchers of Fuzzy Logic, for the construction of three classes of implication operators, giving a variety of results, showing the sensitive effects of Fuzzy operators. In the end, a simple illustration is given, which depicts that the fuzzy systems using implications can be very effectively used in different areas such as process control, modeling, decision making. analysis, etc.