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Defuzzification is used to transform fuzzy inference results into crisp output. The standard defuzzification methods fail in some applications. It is, therefore, important to select appropriate defuzzification methods depending on the application. This paper presents some of the most important defuzzification methods and investigates their properties. With three application examples, it illustrates how to select appropriate defuzzification methods using application specific properties
Published in:
Fuzzy Systems, IEEE Transactions on
(Volume:5
,
Issue:
1
)
Date of Publication: Feb 1997
- Page(s):
- 72 - 79
- ISSN :
- 1063-6706
- INSPEC Accession Number:
- 5506296
- Digital Object Identifier :
- 10.1109/91.554449
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Feb 1997
- Sponsored by :
- IEEE Computational Intelligence Society
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