文章基本信息

标题：A sequential least squares algorithm for ARMAX dynamic network identification ⁎
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作者：Harm H.M. Weerts ; Miguel Galrinho ; Giulio Bottegal 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：844-849
DOI：10.1016/j.ifacol.2018.09.119
语种：English
出版社：Elsevier
摘要：AbstractIdentification of dynamic networks in prediction error setting often requires the solution of a non-convex optimization problem, which can be difficult to solve especially for large-scale systems. Focusing on ARMAX models of dynamic networks, we instead employ a method based on a sequence of least-squares steps. For single-input single-output models, we show that the method is equivalent to the recently developed Weighted Null Space Fitting, and, drawing from the analysis of that method, we conjecture that the proposed method is both consistent as well as asymptotically efficient under suitable assumptions. Simulations indicate that the sequential least squares estimates can be of high quality even for short data sets.
关键词：KeywordsSystem identificationdynamic networksidentification algorithmleast squares