文章基本信息

标题：Piecewise nonlinear regression with data augmentation
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作者：M. Mazzoleni ; V. Breschi ; S. Formentin 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：421-426
DOI：10.1016/j.ifacol.2021.08.396
语种：English
出版社：Elsevier
摘要：AbstractPiecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both a supervised and semi-supervised setting. We further equip the proposed technique with a method for the automatic generation of additional unsupervised data, which are leveraged to improve the overall accuracy of the estimate. The performance of the proposed approach is preliminarily assessed on two simple simulation examples, where we show the benefits of using nonlinear local models and artificially generated unsupervised data.
关键词：KeywordsHybrid System IdentificationNonparametric MethodsNonlinear System Identification