Dependability modelling under uncertainty
著者
書誌事項
Dependability modelling under uncertainty
（Studies in computational intelligence, v. 148）
Springer, c2008
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
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