 Bayesian approaches for on-line robust parameter designO. Vanli and Enrique Del Castillo IISE Transactions, 2009, vol. 41, issue 4, 359-371 Abstract: Two new Bayesian approaches to Robust Parameter Design (RPD) are presented that recompute the optimal control factor settings based on on-line measurements of the noise factors. A dual response model approach to RPD is taken. The first method uses the posterior predictive density of the responses to determine the optimal control factor settings. A second method uses in addition the predictive density of the noise factors. The control factor settings obtained are thus robust not only against on-line variability of the noise factors but also against the uncertainty in the response model parameters. On-line controllable and off-line controllable factors are treated in a unified manner through a quadratic cost function. Both single and multiple-response processes are considered and closed-form robust control laws are provided. Two simulation examples and an example taken from the literature are used to compare the proposed methods with existing RPD approaches that are based on similar models and cost functions.[Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resource: Appendix] Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:41:y:2009:i:4:p:359-371 Ordering information: This journal article can be ordered from DOI: 10.1080/07408170802108534 Access Statistics for this article IISE Transactions is currently edited by Jianjun Shi More articles in IISE Transactions from Taylor & Francis Journals |
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