文章基本信息

标题：Model Migration through Bayesian Adjustments
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作者：Linkai, Luo ; Furong, Gao
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：8
页码：112-116
DOI：10.1016/j.ifacol.2015.08.166
语种：English
出版社：Elsevier
摘要：AbstractModel migration has been proved to be an effective modeling tool to adopt an existing base model from an old process to a similar, yet non-identical process. However, if the process differences are more complex and differ from sample to sample, then the existing model migration strategies can be non-flexible and inadequate. Based on the concepts laid out in an earlier article (Lu and Gao (2008b)), this paper presents an enhanced Bayesian model migration strategy for statistical models. This is achieved by applying Bayesian adjustments to a base model developed using the Gaussian process (GP). The benefits of the proposed method are demonstrated on a continuously stirred tank reactor.
关键词：KeywordsProcess modelsGaussian processesparameter estimationoptimal experimental designefficient algorithms