An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

Marcin Mrugalski

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 1, page 157-169
  • ISSN: 1641-876X

Abstract

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This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.

How to cite

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Marcin Mrugalski. "An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection." International Journal of Applied Mathematics and Computer Science 23.1 (2013): 157-169. <http://eudml.org/doc/251297>.

@article{MarcinMrugalski2013,
abstract = {This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.},
author = {Marcin Mrugalski},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {robust fault detection; non-linear system identification; dynamic GMDH neural network; unscented Kalman filter; nonlinear system identification},
language = {eng},
number = {1},
pages = {157-169},
title = {An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection},
url = {http://eudml.org/doc/251297},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Marcin Mrugalski
TI - An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 1
SP - 157
EP - 169
AB - This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace.
LA - eng
KW - robust fault detection; non-linear system identification; dynamic GMDH neural network; unscented Kalman filter; nonlinear system identification
UR - http://eudml.org/doc/251297
ER -

References

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