Author/Authors
Krzysztof Patan and Thomas Parisini، نويسنده ,
DocumentNumber
1384636
Title Of Article
Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process
شماره ركورد
11276
Latin Abstract
The paper deals with problems of fault detection of industrial processes using dynamic neural networks. The considered neural
network has a feed-forward multi-layer structure and dynamic characteristics are obtained by using dynamic neuron models. Two
optimisation problems are associated with neural networks. The first one is selection of a proper network structure which is solved
by using information criteria such as the Akaike Information Criterion or the Final Prediction Error. In turn, the training of the
network is performed by a stochastic approximation algorithm. The effectiveness of the proposed fault detection and isolation
system is checked using real data recorded in Lublin Sugar Factory, Poland. Additionally, a comparison with alternative approaches
is presented.
From Page
67
NaturalLanguageKeyword
NEURAL NETWORKS , fault detection and isolation , Stochastic approximation , Sensors , Actuators
JournalTitle
Studia Iranica
To Page
79
To Page
79
Link To Document