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2013 | 23 | 1 | 157-169
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
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.
Rocznik
Tom
23
Numer
1
Strony
157-169
Opis fizyczny
Daty
wydano
2013
otrzymano
2012-02-06
poprawiono
2012-09-04
Twórcy
  • Institute of Control and Computation Engineering, University of Zielona Góra, ul. Podgórna 50, 65-246 Zielona Góra, Poland
Bibliografia
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  • Korbicz, J., Kościelny, J., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications, Springer-Verlag, Berlin.
  • Korbicz, J. and Mrugalski, M. (2008). Confidence estimation of GMDH neural networks and its application in fault detection system, International Journal of System Science 39(8): 783-800.
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  • Mrugalski, M. and Korbicz, J. (2007). Least mean square vs. outer bounding ellipsoid algorithm in confidence estimation of the GMDH neural networks, in B. Beliczynski, A. Dzielinski, M. Iwanowski, and B. Ribeiro (Eds.), Adaptive and Natural Computing Algorithms, Part 2, Lecture Notes in Computer Science, Vol. 4432, Physica-Verlag, Heidelberg, pp. 19-26.
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
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