PROPOSAL OF AN INTERPOLATIVE FUZZY INFERENCE METHOD
Manabu Shimakawa, Shuta Murakami · International Journal of General Systems · 2000
In this paper, we propose an interpolative fuzzy inference method, in which the fuzzy relation is represented by the membership functions of the antecedent and consequent parts. The strong point of this method is that the membership function of an inferred conclusion has a simple shape and thus its meaning can be interpreted easily. Firstly, the proposed method is explained, and then it is applied to fuzzy modeling of distributed data. From the modeling result, it was found that the method performed as a possibility distribution model. The proposed method is expected to be effective on a human supervised system, in which a human being takes any action according to the interpretation of a fuzzy inferred conclusion.