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Spatio-Temporal Modeling for Hammerstein Distributed Parameter Systems

  • Han-Xiong Li
  • Chenkun Qi
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 50)

Abstract

A spatio-temporal Hammerstein modeling approach is presented in this chapter. To model the nonlinear distributed parameter system (DPS), a spatio-temporal Hammerstein model (a static nonlinearity followed by a linear DPS) is constructed. After the time/space separation, it can be represented by the traditional Hammerstein system with a set of spatial basis functions. To achieve a low-order model, the Karhunen-Loève (KL) method is used for the time/space separation and dimension reduction. Then a compact Hammerstein model structure is determined by the orthogonal forward regression, and their unknown parameters are estimated with the least-squares method and the singular value decomposition. The simulation and experiment are presented to show the effectiveness of this spatio-temporal modeling method.

Keywords

Model Predictive Control Distribute Parameter System Hammerstein Model NARX Model Hammerstein System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Netherlands 2011

Authors and Affiliations

  • Han-Xiong Li
    • Chenkun Qi

      There are no affiliations available

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