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α‐Cut fuzzy control charts for linguistic data
Author(s) -
Gülbay Murat,
Kahraman Cengiz,
Ruan Da
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20044
Subject(s) - computer science , fuzzy logic , probabilistic logic , set (abstract data type) , data mining , fuzzy set , fuzzy set operations , fuzzy control system , base (topology) , control (management) , artificial intelligence , type 2 fuzzy sets and systems , machine learning , mathematics , programming language , mathematical analysis
The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, α‐cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable α‐level: The higher α the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1173–1195, 2004.