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Sökning: WFRF:(Dreesen Philippe)

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1.
  • Decuyper, Jan, et al. (författare)
  • Decoupling multivariate polynomials for nonlinear state-space models
  • 2019
  • Ingår i: IEEE Control Systems Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2475-1456. ; 3:3, s. 745-750
  • Tidskriftsartikel (refereegranskat)abstract
    • Multivariate polynomials are omnipresent in black-box modelling. They are praised for their flexibility and ease of manipulation yet typically fall short in terms of insight and interpretability. Hence, often an alternative representation is desired. Translating the coupled polynomials into a decoupled form, containing only univariate polynomials has hence become a popular option. In this letter, two new polynomial decoupling techniques are introduced. The features and performance of both methods are illustrated on a nonlinear state-space model identified from data of the forced Duffing oscillator.
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2.
  • Falck, Tillmann, et al. (författare)
  • Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
  • 2012
  • Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 20:11, s. 1165-1174
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.
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