Auxiliary model-based least-squares identification methods for Hammerstein output-error systems

@article{Ding2007AuxiliaryML,
  title={Auxiliary model-based least-squares identification methods for Hammerstein output-error systems},
  author={Feng Ding and Yang Shi and Tongwen Chen},
  journal={Systems & Control Letters},
  year={2007},
  volume={56},
  pages={373-380}
}
The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables—the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm… CONTINUE READING
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