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Defuzzification based on fuzzy clustering
Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 2002We develop a modified fuzzy clustering algorithm for parametric defuzzification in fuzzy rule base systems. Using examples and basic defuzzification properties we compare defuzzification by clustering with the standard defuzzification methods COG (Center of Gravity) and MOM (Mean of Maxima).
H. Genther, T.A. Runkler, M. Glesner
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Improvement possibilities of the maximum defuzzification methods
International Conference on Intelligent Engineering Systems, 2019Mamdani inference model is widely used in that kind of engineering applications, in which imprecision, subjectivity, or uncertainty should be handled in the data and in the evaluation process.
E. Tóth-Laufer
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Fuzzy control and defuzzification
Mechatronics, 1995For two example systems, controlled by either a linear controller or one of five fuzzy controllers (with different defuzzification procedures), responses to step functions are presented in time and in frequency space to qualitatively judge and select the most appropriate defuzzification procedure.
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Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2018
The Analytic Hierarchy Process (AHP) is, in literature, the most frequently used selection method generally in association with fuzzy logic. In this article some incongruities in the use of fuzzy AHP detected in the literature are presented such as ...
Danijela Tuljak Suban, P. Bajec
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The Analytic Hierarchy Process (AHP) is, in literature, the most frequently used selection method generally in association with fuzzy logic. In this article some incongruities in the use of fuzzy AHP detected in the literature are presented such as ...
Danijela Tuljak Suban, P. Bajec
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On the neural defuzzification methods
Proceedings of IEEE 5th International Fuzzy Systems, 2002If representative real world or artificial data sets exist, neural networks can be trained to approximate different defuzzification methods-explicitly known standard methods like center of gravity, extended parametric methods like customisable basic defuzzification distribution, and also black box defuzzification methods.
S.K. Halgamuge, T.A. Runkler, M. Glesner
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[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 2002
Fuzzy systems internally process fuzzy values, which have to be mapped to crisp output in most applications. This conversion is called defuzzification. In many cases the standard algorithms like center of gravity and mean of maxima lead to irrational results. Better defuzzification procedures are required.
T.A. Runkler, M. Glesner
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Fuzzy systems internally process fuzzy values, which have to be mapped to crisp output in most applications. This conversion is called defuzzification. In many cases the standard algorithms like center of gravity and mean of maxima lead to irrational results. Better defuzzification procedures are required.
T.A. Runkler, M. Glesner
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Two new defuzzification techniques
Proceedings of WESCON '93, 2002The term defuzzification in a fuzzy rule-base system refers to the transforming of an action designated in fuzzy terms by the rule-base into a crisp value that can be executed. This paper discusses briefly the three currently most popular defuzzification techniques-the maximizer, centroid and singleton-with their strengths and shortcomings, and then ...
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