A Survey on Fuzzy Implication Functions

One of the key operations in fuzzy logic and approximate reasoning is the fuzzy implication, which is usually performed by a binary operator, called an implication function or, simply, an implication. Many fuzzy rule based systems do their inference processes through these operators that also take c...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2007-12, Vol.15 (6), p.1107-1121
Main Authors: Mas, M., Monserrat, M., Torrens, J., Trillas, E.
Format: Article
Language:English
Subjects:
Agglomeration
Aggregation function
Approximation
discrete implication
Equations
functional equation
Fuzzy
Fuzzy logic
Fuzzy reasoning
Fuzzy set theory
Fuzzy systems
Government
Image processing
implication function
Knowledge based systems
Lattices
Mathematical analysis
Morphology
Operators
Rule based
t-conorm
t-norm
Uncertainty
uninorm
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Summary:One of the key operations in fuzzy logic and approximate reasoning is the fuzzy implication, which is usually performed by a binary operator, called an implication function or, simply, an implication. Many fuzzy rule based systems do their inference processes through these operators that also take charge of the propagation of uncertainty in fuzzy reasonings. Moreover, they have proved to be useful also in other fields like composition of fuzzy relations, fuzzy relational equations, fuzzy mathematical morphology, and image processing. This paper aims to present an overview on fuzzy implication functions that usually are constructed from t-norms and t-conorms but also from other kinds of aggregation operators. The four most usual ways to define these implications are recalled and their characteristic properties stated, not only in the case of [0,1] but also in the discrete case.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2007.896304