文章基本信息

标题：A Sum-Of-Squares Constrained Regression Approach for Process Modeling
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作者：José L. Pitarch ; Antonio Sala ; César de Prada 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：1
页码：754-759
DOI：10.1016/j.ifacol.2019.06.152
语种：English
出版社：Elsevier
摘要：AbstractCombining empirical relationships with a backbone of first-principle laws allow the modeler to transfer the available process knowledge into a model. In order to get such so-called grey-box models, data-reconciliation methods and constrained regression algorithms are key to obtain reliable process models that will be used later for optimization. However, the existent approaches require solving a semi-infinite constrained regression nonlinear problem, which is usually done numerically by an iterative procedure alternating between a relaxed problem and an a posteriori check for constraint violation. This paper proposes an alternative one-stage efficient approach for polynomial regression models based in sum-of-squares (convex) programming. Moreover, it is shown how several desirable features on the regression model can be naturally enforced in this optimization framework. The effectiveness of the proposed ap-proach is illustrated through an academic example provided in the related literature.
关键词：KeywordsConstrained regressionProcess modelsGrey-box modelsSOS programming