Download Applied Artificial Intelligence by Da Duan, Paolo F Fantoni, Martine De Cock, Mike Nachtegael, PDF
By Da Duan, Paolo F Fantoni, Martine De Cock, Mike Nachtegael, Etienne E Kerre
FLINS, initially an acronym for Fuzzy common sense and clever applied sciences in Nuclear technological know-how, is now prolonged to utilized man made Intelligence for utilized learn. The contributions to the 7th within the sequence of FLINS meetings contained during this quantity hide state of the art learn and improvement in utilized synthetic intelligence for utilized study quite often and for power/nuclear engineering specifically.
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Extra resources for Applied Artificial Intelligence
Example text
The scaling function proposed in [1] and [5] can concentrate/dilate the MFs around a single point (a sort of center of gravity), that can be either the center of the universe or one of the two extremes. In [4], the authors propose a more general scaling function, that is a linear composition of sigmoidal-like functions. These two approaches have some limits: the former allows concentrating/dilating the universes around only three predefined points, while the latter requires several parameters, whose identification can be a long and difficult process in the case of strong non-linear behavior of the scaling function.
Real world knowledge is complex and Description Logics or First Order Logic (for which considerable efforts are currently being made in Semantic Web developments) are inadequate. As WA. Woods already pointed out in [1], "many people have responded to the need for increased rigor in knowledge representation by turning to first-order logic as a semantic criterium ... " Humans can still make inferences in an imprecise environment and that's where fuzzy logic comes into the picture. 13 14 Recently, several initiatives have led to reports of connections between Fuzzy Logic and the Semantic Web [2-10].
The structure of wages [10] is studied in economics by means of a set of curves that show how the hourly wage changes with the amount of years of experience. We have different curves depending on the different educational levels of the people: college graduates, college drop-outs, high-school graduates and high-school drop-outs. Hence, we can consider the educational level as the context. The input and the output linguistic variables are uniformly partitioned with 6 and 5 fuzzy sets, respectively.