文章基本信息

标题：An RKHS Approach to Controlling Smoothness in Nonparametric LPV-IO Identification
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作者：Yusuf Bhujwalla ; Vincent Laurain ; Marion Gilson 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：11397-11402
DOI：10.1016/j.ifacol.2017.08.1799
语种：English
出版社：Elsevier
摘要：AbstractAlthough kernel methods have been successfully applied to many different problems in system identification, choosing an optimal kernel structure can be challenging - particularly in higher-order problems. However by noting that structural information, such as linearity, separability and smoothness, is contained in the functional derivatives, it can be seen that the kernel selection problem can be reduced to a simpler regularisation problem over specified derivatives. In this vein, here a novel approach to the control of smoothness in nonparametric LPV identification is proposed. By constraining the derivatives of the scheduling dependencies through a regularisation term, the model smoothness can be linearly controlled through the regularisation hyper parameter, without needing to optimise over the kernel function. A simulation example is presented to show how different structural hypotheses can be tested at minimal extra cost to the user, with the proposed approaches validated against a method from the literature.
关键词：KeywordsLinear Parameter-Varying SystemsSystem IdentificationKernel MethodsReproducing Kernel Hilbert SpacesRegularisationDerivatives in the RKHS