文章基本信息

标题：Deep Learning and System Identification ⁎
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作者：Lennart Ljung ; Carl Andersson ; Koen Tiels 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1175-1181
DOI：10.1016/j.ifacol.2020.12.1329
语种：English
出版社：Elsevier
摘要：AbstractDeep learning is a topic of considerable interest today. Since it deals with estimating - or learning - models, there are connections to the area of System Identification developed in the Automatic Control community. Such connections are explored and exploited in this contribution. It is stressed that common deep nets such as feedforward and cascadeforward nets are nonlinear ARX (NARX) models, and can thus be easily incorporated in System Identification code and practice. The case of LSTM nets is an example of NonLinear State-Space (NLSS) models.
关键词：KeywordsModel structureBias/Variance Trade-offModel ValidationLSTMCascadeforwardnetDeep nets