Gaussian process regression for the estimation of generalized frequency response functions

Jeremy Stoddard, Georgios Birpoutsoukis, Johan Schoukens (Corresponding author), James Welsh

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
4 Downloads (Pure)


Bayesian learning techniques have recently garnered significant attention in the system identification community. Originally introduced for low variance estimation of linear impulse response models, the concept has since been extended to the nonlinear setting for Volterra series estimation in the time domain. In this paper, we approach the estimation of nonlinear systems from a frequency domain perspective, where the Volterra series has a representation comprised of Generalized Frequency Response Functions (GFRFs). Inspired by techniques developed for the linear frequency domain case, the GFRFs are modelled as real/complex Gaussian processes with prior covariances related to the time domain characteristics of the corresponding Volterra series. A Gaussian process regression method is developed for the case of periodic excitations, and numerical examples demonstrate the efficacy of the proposed method, as well as its advantage over time domain methods in the case of band-limited excitations.
Original languageEnglish
Pages (from-to)161-167
Number of pages7
Publication statusPublished - 1 Aug 2019


  • Gaussian process regression
  • Generalized frequency response function
  • Nonlinear system identification


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