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

• Autorzy: Marcin Mrugalski
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

robust fault detection; non-linear system identification; dynamic GMDH neural network; unscented Kalman filter

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 1
• Strony: 157-169

Daty

wydano

2013

otrzymano

2012-02-06

poprawiono

2012-09-04

Twórcy

autor

Marcin Mrugalski

• Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland

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Identyfikatory

DOI

10.2478/amcs-2013-0013