Dependability modelling under uncertainty : An Imprecise probabilistic approach
Mechatronic design processes have become shorter and more parallelized, induced by growing time-to-market pressure. Methods that enable quantitative analysis in early design stages are required, should dependability analyses aim to influence the design. Due to the limited amount of data in this phas...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2008.
Cham : Springer Nature |
| Edice: | Studies in Computational Intelligence
148 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Dependability Modelling under Uncertainty, Texte imprimé, 9783642088803 • Dependability Modelling under Uncertainty, Texte imprimé, 9783540865193 • Dependability modelling under uncertainty, an imprecise probabilistic approach, Philipp Limbourg, Berlin, Springer, 2008, Studies in Computational Intelligence, 978-3-540-69286-7 |
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| 100 | 1 | |a Limbourg, Philipp. | |
| 245 | 1 | 0 | |a Dependability modelling under uncertainty : |b An Imprecise probabilistic approach |c by Philipp Limbourg ; edited by Janusz Kacprzyk. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2008. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 148 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Dependability Prediction in Early Design Stages -- Representation and Propagation of Uncertainty Using the Dempster-Shafer Theory of Evidence -- Predicting Dependability Characteristics by Similarity Estimates A Regression Approach -- Design Space Specification of Dependability Optimization Problems Using Feature Models -- Evolutionary Multi-objective Optimization of Imprecise Probabilistic Models -- Case Study -- Summary, Conclusions and Outlook. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Mechatronic design processes have become shorter and more parallelized, induced by growing time-to-market pressure. Methods that enable quantitative analysis in early design stages are required, should dependability analyses aim to influence the design. Due to the limited amount of data in this phase, the level of uncertainty is high and explicit modeling of these uncertainties becomes necessary. This work introduces new uncertainty-preserving dependability methods for early design stages. These include the propagation of uncertainty through dependability models, the activation of data from similar components for analyses and the integration of uncertain dependability predictions into an optimization framework. It is shown that Dempster-Shafer theory can be an alternative to probability theory in early design stage dependability predictions. Expert estimates can be represented, input uncertainty is propagated through the system and prediction uncertainty can be measured and interpreted. The resulting coherent methodology can be applied to represent the uncertainty in dependability models. | ||
| 776 | 0 | |t Dependability Modelling under Uncertainty |b Texte imprimé |z 9783642088803 | |
| 776 | 0 | |t Dependability Modelling under Uncertainty |b Texte imprimé |z 9783540865193 | |
| 776 | 0 | |0 129849243 |t Dependability modelling under uncertainty |o an imprecise probabilistic approach |f Philipp Limbourg |c Berlin |n Springer |d 2008 |s Studies in Computational Intelligence |z 978-3-540-69286-7 | |
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