Informative input design for Kernel-Based system identification

This paper discusses the design of input sequence for Kernel-Based system identification. From the Bayesian point of view, the kernel reflects a priori information about the target system, which implies that the information obtained from I/O data differs over kernels. This paper focuses on finding a...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 89; pp. 37 - 43
Main Authors Fujimoto, Yusuke, Sugie, Toshiharu
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.03.2018
Subjects
Bayesian estimation
Experiment design
Information theory
Kernel-Based system identification
Kernel-Based system identification
Experiment design
Bayesian estimation
Information theory
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Summary:This paper discusses the design of input sequence for Kernel-Based system identification. From the Bayesian point of view, the kernel reflects a priori information about the target system, which implies that the information obtained from I/O data differs over kernels. This paper focuses on finding an input sequence which maximizes the information obtained through an observation according to the kernel which is given in advance. As an appropriate measure of such information, the mutual information is adopted. For the given kernel, a concrete procedure is proposed to find the input sequence maximizing the mutual information subject to the input energy constraints. Numerical examples are given to illustrate the effectiveness of the proposed input design. Furthermore, it is shown analytically that the impulse input is optimal for a special class of kernels.
ISSN:0005-1098
DOI:10.1016/j.automatica.2017.11.019