Abstract
The information entropy introduced by Shannon is a measure which represents the value of information in numerical value. Generally, when the result is obvious before receiving related information, the value of information is low. On the contrary, the more ambiguous the result is, the higher the value of information becomes. Following Shannon's information entropy, we can assume the ambiguous object as the fuzziness of fuzzy sets. For example, if some of whose factors are "very high dollar value" and "high dollar value" is included in the whole sets, "the rate of exchange", the fuzziness of "very high dollar value" is smaller than that of "high dollar value". In this paper we report a consideration of methods for approximate reasoning employing a fuzzy entropy theory as the parameter of the defuzzification operation after composition of "THEN" conclusive part membership function. A desuzzifier methods called fuzzy entropy method are realized as robust natures as any the area method, hight method, and the center of gravity method used usually in the approximate reasoning.
