Calculus of Interpolated Fuzzy Relation Type Fuzzy Reasoning Method

Author

Shimakawa, Manabu

Author_Institution

Dept. of Inf. & Comput. Sci., Kumamoto Nat. Coll. of Technol.

Volume

2

fYear

2006

fDate

Aug. 30 2006-Sept. 1 2006

Firstpage

325

Lastpage

328

Abstract

Interpolated fuzzy relation type (IFRT) fuzzy reasoning method has the following two features: (1) the membership function of a reasoning result fuzzy set is not likely to produce a complicated shape, so it is easy to interpret its meaning, (2) if the fuzziness of input fuzzy set increases, the fuzziness of the reasoning result fuzzy set also increases. Reasoning process of IFRT method is simple in case of given a real number as input. But fuzzy number input case, its reasoning process is not easy as compared with real number input case. This paper shows practical calculus technique of reasoning process for real number input case and fuzzy number input case with concrete example

Keywords

calculus; fuzzy reasoning; fuzzy set theory; interpolation; number theory; calculus technique; fuzzy number input case; fuzzy set; interpolated fuzzy relation type fuzzy reasoning method; membership function; real number input case; Calculus; Concrete; Educational institutions; Fuzzy reasoning; Fuzzy sets; Humans; Input variables; Interpolation; Predictive models; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on

Conference_Location

Beijing

Print_ISBN

0-7695-2616-0

Type

conf

DOI

10.1109/ICICIC.2006.247

Filename

1691992