Using of self-teaching systems for steel brand identification in oxygen converter prodution
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Authors:
T.A. Zheldak, Candidate of Technical Sciences, Senior Lecturer of the System Analysis And Control Department of the State Higher Educational Institution “National Mining University”, Dnipropetrovsk, Ukraine
N.A. Kucherenko, Student of State Higher Educational Institution “National Mining University”, Dnipropetrovsk, Ukraine
The analysis of the technological process of low-carbon steel in basic oxygen furnace and the information known before purging starts. To classify the grade of steel it was selected the self-learning system in the form of a two-layer neural network based on perceptrons. Experimental studies on training and use of the network have been carried out. Conclusions about the network and its learning algorithm have been drawn. Suggestions of probable improvement of the system have been formulated.
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