Results 181 to 190 of about 18,737 (215)

Radial defuzzification method

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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Fuzzy tests – defuzzification and randomization

Fuzzy Sets and Systems, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On the neural defuzzification methods

Proceedings of IEEE 5th International Fuzzy Systems, 2002
If 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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A set-theoretical defuzzification method

Fuzzy Sets and Systems, 1995
A 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

2009
We 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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Empirical study of defuzzification

22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003, 2004
The most important application of fuzzy logic is designing controllers. Fuzzy logic controllers (FLC) are much easier to design than non-linear controllers of similar capabilities. The rules that a designer needs to create are often based on their current experience and knowledge.
S.S. Lancaster, M.J. Wierman
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Advanced Inference Filter Defuzzification

2001
In 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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Combinability functions in defuzzification

Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, 2002
We overview various knowledge-based defuzzification functions from a system modeling and controls perspective, and introduce a new variant which is useful in applications where fuzzy variables are related to deviations from a norm, such as feedback control. We also summarize results of simulation experiments.
D. Swiatlowski, C. Isik
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Fuzzy control and defuzzification

Mechatronics, 1995
For 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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Central values of fuzzy numbers—defuzzification

Information Sciences, 1997
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