Titlebook: Dependability Modelling under Uncertainty; An Imprecise Probabi Philipp Limbourg Book 2008 Springer-Verlag Berlin Heidelberg 2008 Computati

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https://doi.org/10.1007/978-3-0348-5258-6design problems with a high complexity and several objectives [26]. Hence, MOEAs seem to be an interesting option for dependability optimization in an EDS. However most MOEAs are designed for deterministic problems, requiring that an evaluation of the objectives for a solution results in a determini

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Durch Symmetrie die moderne Physik verstehene field. The object of interest, an automatic transmission system under development emanates from the ZF AS Tronic family. The study represents the contribution of the University of Duisburg-Essen to the ESReDA (European Safety, Reliability & Data Association, [51]) workgroup on uncertainty analysis

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https://doi.org/10.1007/978-3-662-53812-8s are to be developed and refined. These methods need to consider the various sources which introduce a large amount of uncertainty into the prediction. As a consequence, predictions must include uncertainty models. Beside probability theory, several other frameworks for representing and propagating

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https://doi.org/10.1007/978-3-540-69287-4Computational Intelligence; Dependability Modelling; Mechatronics; Regression; System; Uncertainty; modeli

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