Cost function shaping of the output error criterion

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• 作者：Diego Eckhard^a ; ^{diegoeck@ufrgs.br}Author Vitae ; Alexandre S. Bazanella^b ; ^{bazanella@ufrgs.br}Author Vitae ; Cristian R. Rojas^c ; ^{cristian.rojas@ee.kth.se}Author Vitae ; Hå ; kan Hjalmarsson^c ; ^{hakan.hjalmarsson@ee.kth.se}Author Vitae
• 关键词：Identification methods ; Model fitting
• 刊名：Automatica
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：76
• 期：Complete
• 页码：53-60
• 全文大小：792 K
• 卷排序：76

文摘

Identification of an output error model using the prediction error method leads to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because in most cases both the corresponding objective function and the search space are nonconvex. The difficulty in solving the optimization problem depends mainly on the experimental conditions, more specifically on the spectra of the input/output data collected from the system. It is therefore possible to improve the convergence of the algorithms by properly choosing the data prefilters; in this paper we show how to perform this choice. We present the application of the proposed approach to case studies where the standard algorithms tend to fail to converge to the global minimum.