文章基本信息

标题：Input selection in N2SID using group lasso regularization
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作者：Måns Klingspor ; Anders Hansson ; Johan Löfberg 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：9474-9479
DOI：10.1016/j.ifacol.2017.08.1472
语种：English
出版社：Elsevier
摘要：AbstractInput selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.
关键词：KeywordsInput selectionSystem identificationState-space modelsN2SIDSubspace methodsSignal-to-noise ratio