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Improvement of Cold Mill Precalculation Accuracy Using a Corrective Neural Network
Jang, Min,Cho, Sungzoon,Cho, Yong-Joong,Yoon, Sungcheol,Cho, Hyungsuk 한국경영과학회 1996 한국경영과학회 학술대회논문집 Vol.- No.1
Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. At Pohang Iron and Steel Company (POSCO) in Pohang, Korea, precalculation determines the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to improve the accuracy of the roll force prediction. Additional variables to be fed to the network include the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 2,277 process data collected from POSCO from March 1995 through December 1995, then was tested on the unseen 200 data from the same period. The combined model reduced the prediction error by 55.4% on average.
Time Series Prediction using Virtual Term Generation Scheme
Jo, Taeho,Cho, Sungzoon 한국경영과학회 1996 한국경영과학회 학술대회논문집 Vol.- No.1
The values measured at different time and enumerated sequentially by homogenous interval is called time series. Its goal is to predict values in future by analysing the measured values in past. The stastical approach to time series prediction tend to be by a neural approach with difficulties in expressing the reationship among past data. In neural approach, the preblem is the acquisition of the enough training data in advance. The goal of this paper is that such problem is solved by generating another term as virtual term between terms in time series.