Results 181 to 190 of about 6,233 (217)
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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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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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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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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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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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Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 1994
Till now the problem of defuzzification has not been explained very specifically. Conceptually it is not as simple a problem as it looks. It is one of fundamental problems of selection and decision making. In this paper the authors attempt to understand the problem from the perspective of the neighbors.
S. Roychowdhury, B.H. Wang, S.K. Ahn
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Till now the problem of defuzzification has not been explained very specifically. Conceptually it is not as simple a problem as it looks. It is one of fundamental problems of selection and decision making. In this paper the authors attempt to understand the problem from the perspective of the neighbors.
S. Roychowdhury, B.H. Wang, S.K. Ahn
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A set-theoretical defuzzification method
Fuzzy Sets and Systems, 1995A defuzzification method coinciding with the result of the subsethood theorem, and including a family of averaging methods (like mean of maxima and center of gravity) as special cases is presented. The defuzzification method, named subsethood defuzzification (SD) method, has the advantage of being exclusively based on set theoretical operations, giving
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Defuzzification of Fuzzy p-Values
2009We provide a new description of the notion of fuzzy p-value, within the context of the theory of imprecise probabilities. The fuzzy p-value is viewed as a representation of a certain second-order possibility measure. According to Walley, any second-order possibility measure can be converted into a pair of lower and upper probabilities.
Inés Couso, Luciano Sánchez
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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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Advanced Inference Filter Defuzzification
2001In the field of modeling, fuzzy models are one efficient approach for representing technical systems or human control strategies. Fuzzy models have the advantage of supplying a transparent and interpretable model. Conventional fuzzy models are based on fuzzification, inference and defuzzification.
Harro Kiendl, Paul Krause
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