Tuning the hyperparameters of the filter-based regularization method for impulse response estimation

Anna Marconato, Maarten Schoukens

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

This paper discusses the use of a filter-based method for regularized impulse response modeling for linear time-invariant systems. The proposed method is a reformulation of the Bayesian, kernel based impulse response modeling approaches. The filter interpretation of the regularization cost function allows one to develop an intuitive framework to model a wide range of systems with different properties in a flexible way. Two hyperparameter selection techniques, based on Cross Validation and on Marginal Likelihood Maximization are presented. The proposed methods are tested on Monte Carlo simulation examples and on a real robotics problem. The results are compared with the ones obtained with the kernel-based methods based on the DC and TC kernels.
Original languageEnglish
Pages (from-to)12841-12846
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes
Event20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org/

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