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This paper introduces different levels of fuzziness into the class of UML and presents the corresponding graphical representations, with the result that UML class diagrams may model fuzzy information.
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In the ANFIS model, fuzzy subtractive clustering algorithm was used to design an initial rule base.
Logic-based models include Boolean logic models, multi-state discrete models, and fuzzy logic models [38], [39].
Furthermore, some of major parameter of the model such as demand rate, deterioration rate, holding cost, and ordering cost may be fuzzy variable.
Who provoked whom and why may remain fuzzy.
The details may be fuzzy, but the news is good for all that.
Some images may appear fuzzy, particularly in colocalizing areas/vesicles.
Your song may sound fuzzy.
The model of fuzzy logic control consists of a fuzzier, a fuzzy inference engine, and a defuzzier.
Note that other fuzzy linguistic models may be considered to model the portion of criteria to be satisfied in the aggregation step, e.g., few, some, half, many, or most of the features can be required to be strongly correlated.
Furthermore, some of parameter of the model may be either fuzzy or random variable.
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