Improved frequency response function estimation by Gaussian process regression with prior knowledge

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3 Citations (Scopus)

Abstract

Kernel-based modelling of dynamical systems offers important advantages such as imposing stability, causality and smoothness on the estimate of the model. Here, we improve the existing frequency domain kernel-based approach for estimating the transfer function of a linear time-invariant system from noisy data. This is done by introducing prior knowledge in the kernel. We use a local rational modelling technique to determine the most significant poles, and include these poles as prior knowledge in the kernel. This results in accurate models for the identification of lightly-damped systems.
Original languageEnglish
Pages (from-to)559-564
Number of pages6
JournalIFAC-PapersOnLine
Volume54
Issue number7
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • Data-driven modelling
  • kernel-based
  • Gaussian process regression
  • machine learning
  • lightly damped systems
  • local rational modelling

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