Results 171 to 180 of about 18,737 (215)
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Averaging procedures in defuzzification processes
Fuzzy Sets and Systems, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eugene Roventa, Tiberiu Spircu
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A note on the set-theoretical defuzzification
Fuzzy Sets and Systems, 1998The set-theoretic defuzzification (SD) method has been claimed to be a rational defuzzification method which is exclusively based on the fuzzy set theory [J. Valente de Oliveira, Fuzzy Sets and Systems 76 (1995) 63]. However, this paper shows the examples that the method is not applicable with general t-norms and t-conorms.
Kim, JK Kim, Jong-Kyou +2 more
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Defuzzification using Steiner points
Fuzzy Sets and Systems, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thomas Vetterlein, Mirko Navara
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Defuzzification in Scenario Management
2023It is virtually a truism that companies should respond to situations of high complexity, dynamics and contingency with strategic management measures. Not least in turbulent VUCA-type environments, the company is well advised to use alternative scenarios for environmental forecasting. The so-called scenario technique is used for this purpose.
Spengler, Thomas, Herzog, Sebastian
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Defuzzification with Constraints
1995We look at the problem of defuzzification in situations where in addition to the usual fuzzy output of the controller there exists some ancillary restriction on the allowable defuzzified values. We provide two basic approaches to address this problem. In the first approach we enforce the restriction by selecting the defuzzified value through a random ...
Ronald R. Yager, Dimitar P. Filev
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A defuzzification method respecting the fuzzification
Fuzzy Sets and Systems, 1997Abstract When designing a fuzzy controller, the first thing to do is to choose input and output fuzzifications. The next step consists of building the fuzzy rules table describing the behaviour of the controller. Finally, in order to convert the fuzzy output in a usable form, we need to select a defuzzification strategy.
L. Rondeau +3 more
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On Orientation Sensitive Defuzzification Functionals
2014The aim of the article is to investigate defuzzification functionals in the theory of Ordered Fuzzy Numbers (OFN). The model of OFN was introduced in 2002 to overcome drawbacks of classical (convex) fuzzy numbers. Each OFN is equipped with an additional feature – the orientation. New forms of defuzzification functionals are proposed which are sensitive
Tomek Bednarek +2 more
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DEFUZZIFICATION WITHIN A MULTICRITERIA DECISION MODEL
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2003In many cases, criterion values are crisp in nature, and their values are determined by economic instruments, mathematical models, and/or by engineering measurement. However, there are situations when the evaluation of alternatives must include the imprecision of established criteria, and the development of a fuzzy multicriteria decision model is ...
Serafim Opricovic, Gwo-Hshiung Tzeng
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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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1995
We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Furthermore, we give an alternative approach for the case where the output fuzzys sets have different shapes. Finally, we give an example.
H. Hellendoorn, C. Thomas
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We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Furthermore, we give an alternative approach for the case where the output fuzzys sets have different shapes. Finally, we give an example.
H. Hellendoorn, C. Thomas
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