@misc{Zítek_Pavel_Neural,
 author={Zítek, Pavel and Mánková, Renata and Hlava, Jaroslav},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose.},
 abstract={Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs).},
 abstract={This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.},
 type={artykuł},
 title={Neural network evaluation of model-based residuals in fault detection of time delay systems},
 keywords={model-based fault detection, anisochronic model, state observer, internal model control, artificial neural networks},
}