Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems

@article{Wang2008ExtendedSG,
  title={Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems},
  author={Dongqing Wang and Feng Ding},
  journal={Computers & Mathematics with Applications},
  year={2008},
  volume={56},
  pages={3157-3164}
}
An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of… CONTINUE READING
Highly Cited
This paper has 153 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 79 extracted citations

154 Citations

02040'10'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 154 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 20 references

Gradient-based identificationmethods for Hammerstein nonlinear ARMAXmodels

  • F. Ding, Y. Shi, T. Chen
  • Nonlinear Dynamics 45 (1–2)
  • 2006
Highly Influential
7 Excerpts

Three methods of separating parameters for Hammerstein nonlinear systems

  • F. Wei, F. Ding
  • Science Technology and Engineering 8 (6)
  • 2008
1 Excerpt

System identification based on Hammerstein model

  • F. Z. Chaoui, F. Giri, Y. Rochdi, M. Haloua, A. Naitali
  • International Journal of Control 78 (6)
  • 2005
2 Excerpts

Similar Papers

Loading similar papers…