Generalized kernel regression estimatefor the identification of Hammerstein systems

Grzegorz Mzyk

International Journal of Applied Mathematics and Computer Science (2007)

  • Volume: 17, Issue: 2, page 189-197
  • ISSN: 1641-876X

Abstract

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A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.

How to cite

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Mzyk, Grzegorz. "Generalized kernel regression estimatefor the identification of Hammerstein systems." International Journal of Applied Mathematics and Computer Science 17.2 (2007): 189-197. <http://eudml.org/doc/207831>.

@article{Mzyk2007,
abstract = {A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.},
author = {Mzyk, Grzegorz},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {Hammerstein system; kernel estimation; nonparametric regression},
language = {eng},
number = {2},
pages = {189-197},
title = {Generalized kernel regression estimatefor the identification of Hammerstein systems},
url = {http://eudml.org/doc/207831},
volume = {17},
year = {2007},
}

TY - JOUR
AU - Mzyk, Grzegorz
TI - Generalized kernel regression estimatefor the identification of Hammerstein systems
JO - International Journal of Applied Mathematics and Computer Science
PY - 2007
VL - 17
IS - 2
SP - 189
EP - 197
AB - A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.
LA - eng
KW - Hammerstein system; kernel estimation; nonparametric regression
UR - http://eudml.org/doc/207831
ER -

References

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