<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

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

International Journal of Applied Mathematics and Computer Science

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...

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

Publié en ligne: 26 mars 2013

Pages: 157 - 169

DOI: https://doi.org/10.2478/amcs-2013-0013

This content is open access.