A hybrid approach to outlier detection in the offset lithographic printing process |
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| Affiliation: | 1. Intelligent Systems Laboratory, School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, S 30118 Halmstad, Sweden;2. Department of Applied Electronics, Kaunas University of Technology, LT-3031 Kaunas, Lithuania;1. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland;2. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland;1. Laboratoire d''Automatique et de Robotique, Département d’Électronique, Faculté des sciences de l''ingénieur, Université des Freres Mentouri Constantine, Route d''Ain el bey, 25000 Constantine, Algeria;2. Département des Sciences Exactes et Informatique, Ecole Normale Supérieure de Constantine, Ali Mendjli, Constantine 3, Algeria;3. IBISC Laboratory, University Evry val D''Essonnes, 40 Pelvoux Street, 91080 EVRY Courcouronnes Cedex, France |
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| Abstract: | Artificial neural networks are used to model the offset printing process aiming to develop tools for on-line ink feed control. Inherent in the modelling data are outliers owing to sensor faults, measurement errors and impurity of materials used. It is fundamental to identify outliers in process data in order to avoid using these data points for updating the model. We present a hybrid, the process-model-network-based technique for outlier detection. The outliers can then be removed to improve the process model. Several diagnostic measures are aggregated via a neural network to categorize data points into the outlier and inlier classes. We demonstrate experimentally that a soft fuzzy expert can be configured to label data for training the categorization of neural network. |
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