文章基本信息

标题：Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems ⁎
本地全文：下载
作者：Mengyuan Fang ; Miguel Galrinho ; Håkan Hjalmarsson 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：345-350
DOI：10.1016/j.ifacol.2021.08.383
语种：English
出版社：Elsevier
摘要：AbstractRecursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.
关键词：KeywordsRecursive system identificationmultivariate systemsweighted null-space fitting