System identification through online sparse Gaussian process regression with input noise

Hildo Bijl, T.B. Schön, Jan-Willem van Wingerden, Michel Verhaegen

Research output: Contribution to journalArticleScientificpeer-review


There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have three important downsides: (1) it is computationally intensive, (2) it cannot efficiently implement newly obtained measurements online, and (3) it cannot deal with stochastic (noisy) input points. In this paper we present an algorithm tackling all these three issues simultaneously. The resulting Sparse Online Noisy Input GP (SONIG) regression algorithm can incorporate new noisy measurements in constant runtime. A comparison has shown that it is more accurate than similar existing regression algorithms. When applied to nonlinear black-box system modeling, its performance is competitive with existing nonlinear ARX models.
Original languageEnglish
Number of pages11
JournalIFAC Journal of Systems and Control
Publication statusPublished - 2017


  • Nonlinear system identification
  • Gaussian processes
  • Regression
  • Machine learning
  • Sparse methods


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