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Type-2 fuzzy sets made simple
Abstract
Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems. However, they are difficult to understand for a variety of reasons which we enunciate. In this paper, we strive to overcome the difficulties by: (1) establishing a small set of terms that let us easily communicate about type-2 fuzzy sets and also let us define such sets very precisely, (2) presenting a new representation for type-2 fuzzy sets, and (3) using this new representation to derive formulas for union, intersection and complement of type-2 fuzzy sets without having to use the Extension Principle.
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Cited by
Interval Type-2 Fuzzy Logic Systems Made Simple
Mendel, J. M., John, R. I., & Liu, F. (2006) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.879986 · Cited by 2,112
Self-citationOn the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers
Wu, D. (2012) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2012.2186818 · Cited by 339
Geometric Type-1 and Type-2 Fuzzy Logic Systems
Coupland, S., & John, R. (2007) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2006.889764 · Cited by 316
Self-citationÇolak, M., & Kaya, İ. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.194 · Cited by 312
General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial
Mendel, J. M. (2014) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2013.2286414 · Cited by 311
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Metadata
- Authors
- Mendel, J. M., & John, R. I. B.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2002-04
- Volume
- 10
- Issue
- 2
- Pages
- 117-127
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2132885717
- Cited by
- 2,616
- References
- 0
- FWCI
- 19.07
- Type
- journal article
- Language
- en
- Open access
- closed
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