Design Optimization with Imprecise Random Variables

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dc.contributor.authorHerrmann, Jeffrey W.
dc.date.accessioned2008-09-29T14:06:02Z
dc.date.available2008-09-29T14:06:02Z
dc.date.issued2008-09
dc.description.abstractDesign optimization is an important engineering design activity. Performing design optimization in the presence of uncertainty has been an active area of research. The approaches used require modeling the random variables using precise probability distributions or representing uncertain quantities as fuzzy sets. This work, however, considers problems in which the random variables are described with imprecise probability distributions, which are highly relevant when there is limited information about the distribution of a random variable. In particular, this paper formulates the imprecise probability design optimization problem and presents an approach for solving it. We present examples for illustrating the approach.en
dc.format.extent289145 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/8431
dc.language.isoen_USen
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.relation.ispartofseriesTR 2008-28en
dc.subjectdesign optimizationen
dc.subjectimprecise probabilitiesen
dc.titleDesign Optimization with Imprecise Random Variablesen
dc.typeTechnical Reporten

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