International Journal of applied mathematics and computer science

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Paper details

Number 1 - March 2013
Volume 23 - 2013

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

Marcin Mrugalski

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.

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

DOI
10.2478/amcs-2013-0013