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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.
  • Lee, T. and Jiang, Z. (2006). On uniform global asymptotic stability of nonlinear discrete-time systems with applications, IEEE Transactions on Automatic Control 51(10): 1644-1660.
  • Ljung, L. (1999). System Identification: Theory for the User, Prentice Hall PTR, Upper Saddle River, NJ.
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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

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