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A flexible state-space model for learning nonlinear dynamical systems
Svensson, A.; Schön, T.B. (2017). A flexible state-space model for learning nonlinear dynamical systems. Automatica 80: 189-199. https://dx.doi.org/10.1016/j.automatica.2017.02.030
In: Automatica. PERGAMON-ELSEVIER SCIENCE LTD: Oxford. ISSN 0005-1098; e-ISSN 1873-2836, more
Peer reviewed article  

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Keywords
    Others
    Others > Modelling & Prediction
    Scientific Community
    Scientific Publication
    Software/Modelling Tool
Author keywords
    System identification; Nonlinear models; Regularization; Probabilisticmodels; Bayesian learning; Gaussian processes; Monte Carlo methods

Project Top | Authors 
  • Association of European marine biological laboratories, more

Authors  Top 
  • Svensson, A.
  • Schön, T.B.

Abstract
    We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions. The coefficients in the basis function expansions are learned from data. Using a connection to Gaussian processes we also develop priors on the coefficients, for tuning the model flexibility and to prevent overfitting to data, akin to a Gaussian process state-space model. The priors can alternatively be seen as a regularization, and helps the model in generalizing the data without sacrificing the richness offered by the basis function expansion. To learn the coefficients and other unknown parameters efficiently, we tailor an algorithm using state-of-the-art sequential Monte Carlo methods, which comes with theoretical guarantees on the learning. Our approach indicates promising results when evaluated on a classical benchmark as well as real data.

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