Type-2 fuzzy logic systems

We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 1999-12, Vol.7 (6), p.643-658
Main Authors: Karnik, N.N., Mendel, J.M., Qilian Liang
Format: Article
Language:English
Subjects:
Channels
Computation
Crisps
Dispersion
Equalization
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Image processing
Nearest neighbor searches
Reduction
Signal processing
Time-varying channels
Training data
Uncertainty
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Summary:We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.811231