Knowledge acquisition in fuzzy reasoning

Kaoru Hirota*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

Summary form only given. Fuzzy reasoning is the fuzzy extension of inference operations used in AI or expert systems. Among the various fuzzy reasoning methods, rule-based fuzzy reasoning is the most important from a viewpoint of industrial applications. In Japan, industrial fuzzy expert systems have been developed since the early 1980s, especially in the area of control. In such applications the control knowledge of human experts is represented by a set of so-called fuzzy production rules. In such a fuzzy knowledge base, the size is generally rather small, since the fuzzy (or ambiguous) descriptions are allowed, so the knowledge-acquisition process is not so difficult compared with ordinary (or crisp) expert systems, where the number of rules is more than a few thousand. A question-and-answer method is used in the first step, then the parameter tuning is done, and finally a complete set of fuzzy rules is obtained.

Original languageEnglish
Pages1363
Number of pages1
Publication statusPublished - 1989
Externally publishedYes
EventSICE '89: Proceedings of the 28th SICE Annual Conference Volume 2 (of 2) - Tokyo, Jpn
Duration: 25 Jul 198927 Jul 1989

Conference

ConferenceSICE '89: Proceedings of the 28th SICE Annual Conference Volume 2 (of 2)
CityTokyo, Jpn
Period25/07/8927/07/89

Fingerprint

Dive into the research topics of 'Knowledge acquisition in fuzzy reasoning'. Together they form a unique fingerprint.

Cite this