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M Mizumoto, H-J Zimmerman (1982)
Comparison on fuzzy reasoning methodsFuzzy Sets Syst., 8
A Giurca, I Iancu (2006)
Computational Intelligence, Theory and Applications - Int. Conf. 9th Fuzzy Days in Dortmund
S Fukami, M Mizumoto, M Tanaka (1980)
Some considerations on fuzzy conditional inferenceFuzzy Sets Syst., 4
R Fuller (1995)
Neural Fuzzy Systems
I Iancu (2003)
On a representation of an uncertain body of evidenceAnnals Univ. Craiova Math. Comput. Sci. serie, XXX
D Pacholczyk (1987)
Introduction d’un seul dans le calcul de l’incertitude en logique floueBUSEFAL, 32
E Czogala, J Leski (2001)
On equivalence of approximate reasoning results using different interpolations of fuzzy if-then rulesFuzzy Sets Syst., 11
LA Zadeh (1979)
A theory of approximate reasoningMach. Intell., 9
RR Yager (1980)
An approach to inference in approximate reasoningInt. J. Man Mach. Stud., 13
LA Zadeh (1975)
Fuzzy Sets and their Applications to Cognitive and Decision Processes
EM Mamdani (1977)
Application of fuzzy logic to approximate reasoning using linguistic systemsTrans. Comput., 26
D Dubois, H Prade (1984)
Fuzzy logics and the generalized modus ponens revisitedCybern. Syst., 15
I Iancu (2005)
Operators with n-thresholds for uncertainty managementInt. J. Appl. Math. Comput., 19
I Iancu (1997)
T-norms with thresholdFuzzy Sets Syst., 85
I Iancu (2009)
Generalized modus ponens using fodor’s implication and T-norm product with thresholdInt. J. Comput. Commun. Control, IV
LA Zadeh (1978)
Fuzzy sets as a basis for a theory of a possibilityFuzzy Sets Syst., 1
M Baczynski, B Jayaram (2008)
Fuzzy implications. Studies in Fuzzines and Soft Computing, vol. 231
I Iancu (1998)
Propagation of uncertainty and imprecision in knowledge-based systemsFuzzy Sets Syst., 94
G Klir, B Yuan (1995)
Fuzzy Sets and Fuzzy Logic
M Mizumoto (1985)
Fuzzy reasoning under new compositional rule of inferenceKybernetics, 12
Using Generalized Modus Ponens reasoning, we examine the values of the inferred conclusion depending on the correspondence between the premise of the rule and the observed fact. The conclusion is obtained using a set of implications in order to represent a fuzzy if-then rule with a single input single output and the t-norm with threshold generated by t-norm T(x,y) = xy, as a compositional operator. Some comments and an example are presented in order to show how the obtained results can be used.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Oct 23, 2013
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