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Modelling and multi-innovation parameter identification for Hammerstein nonlinear state space systems using the filtering technique

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  • Xuehai Wang
  • Feng Ding

Abstract

This article focuses the problem of the modelling and identification for Hammerstein state space systems with coloured noise. In order to jointly estimate the system parameters and states, a filtering-based multi-innovation stochastic gradient algorithm is developed by combining the filtering technique with the multi-innovation identification theory. The key is that the estimation of the system parameters uses the estimated states, and the estimation of the states uses the preceding parameter estimates. The given examples confirm that the proposed algorithm can provide more accurate parameter estimates than the hierarchical multi-innovation stochastic gradient algorithm.

Suggested Citation

  • Xuehai Wang & Feng Ding, 2016. "Modelling and multi-innovation parameter identification for Hammerstein nonlinear state space systems using the filtering technique," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 22(2), pages 113-140, March.
    • Handle: RePEc:taf:nmcmxx:v:22:y:2016:i:2:p:113-140
    DOI: 10.1080/13873954.2016.1142455
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    References listed on IDEAS

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    1. Jia Wang & Hong-Wei Wang & Hong Gu, 2013. "A novel recursive subspace identification approach of closed-loop systems," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(6), pages 526-539, December.
    2. Christoph Hametner & Markus Stadlbauer & Maxime Deregnaucourt & Stefan Jakubek, 2013. "Incremental optimal process excitation for online system identification based on evolving local model networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(6), pages 505-525, December.
    3. Yun Zeng & Yakun Guo & Lixiang Zhang & Tianmao Xu & Hongkui Dong, 2013. "Nonlinear hydro turbine model having a surge tank," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(1), pages 12-28.
    4. M. Milanese & C. Novara & L. Pivano, 2005. "Structured SM identification of vehicle vertical dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 11(2), pages 195-207, June.
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