Bayesian approaches for on-line robust parameter design
Bayesian approaches for on-line robust parameter design
复制标题
DOI：
10.1080/07408170802108534
复制
发表时间：
2009-01
期刊：
影响因子：
--
通讯作者：
O. A. Vanli;Enrique del Castillo
中科院分区：
文献类型：
--
作者：
O. A. Vanli;Enrique del Castillo
提出了两种新的贝叶斯鲁棒参数设计（RPD）方法，基于噪声因子的在线测量重新计算最优控制因子设置。采用双响应模型方法对RPD进行了研究。第一种方法使用响应的后验预测密度来确定最佳控制因子设置。第二种方法另外使用噪声因子的预测密度。因此，所获得的控制因子设置不仅对噪声因子的在线变化，而且对响应模型参数的不确定性是鲁棒的。通过二次型代价函数统一处理了在线可控和离线可控因素。单响应和多响应过程被认为是封闭形式的鲁棒控制律。两个模拟的例子和一个例子从文献中被用来比较所提出的方法与现有的RPD方法是基于类似的模型和成本函数。[可提供本条的补充材料。访问出版商的在线版IIE Transactions，获取以下免费补充资源：附录]
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]