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标题：Extension of First-Order Stable Spline Kernel to Encode Relative Degree * * This work is supported by Grant-in-Aid for JSPS Research Fellow grant number JP15J05700, JSPS KAKENHI grant number JP16H06093, and JSPS KAKENHI grant number JP16K14284
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作者：Yusuke Fujimoto ; Ichiro Maruta ; Toshiharu Sugie 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：14016-14021
DOI：10.1016/j.ifacol.2017.08.2425
语种：English
出版社：Elsevier
摘要：AbstractThis paper focuses on the kernel-based system identification methods, which estimate the impulse response of the target system in the Bayesian estimation framework. This paper discusses about continuous-time systems, and proposes a new kernel based on a prior that the relative degree of the target system is higher than or equal to two. Such a prior is identical to a prior on the continuity of the impulse response at time zero. The proposed kernel is an extension of the first-order Stable Spline kernel, which is one of the most famous kernels. Numerical examples are shown to demonstrate the effectiveness of the proposed kernel.
关键词：KeywordsSystem identificationnon-parametric estimationimpulse responsesBayesian estimation