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Identification of continuous-time models for nonlinear dynamic systems from discrete data

Author

Listed:
  • Yuzhu Guo
  • Ling Zhong Guo
  • Stephen A. Billings
  • Hua-Liang Wei

Abstract

A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed.

Suggested Citation

  • Yuzhu Guo & Ling Zhong Guo & Stephen A. Billings & Hua-Liang Wei, 2016. "Identification of continuous-time models for nonlinear dynamic systems from discrete data," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 3044-3054, September.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:12:p:3044-3054
    DOI: 10.1080/00207721.2015.1069906
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    References listed on IDEAS

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    1. Yuzhu Guo & L.Z. Guo & S.A. Billings & Hua-Liang Wei, 2015. "An iterative orthogonal forward regression algorithm," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 776-789, April.
    2. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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