@inproceedings{7387e01ddece43e599fbbd1ac1a6321d,
title = "Application of SVM regression in HAGC system",
abstract = "This paper puts forward a design which is presented to estimate relatively accurate HAGC control system and then to predict the rolling gap. Considering many factors that influence the precision of the rolling gap, we can obtain the final formula of the rolling gap according to the theoretical calculation. Besides, A SVM (support vector machine) regression model based on the machine learning is proposed and applied to predict the rolling gap. According to the rolling data collected in the working field, we train SVM Regression model of the rolling gap, then the predicted rolling gap is achieved in the light of the SVM model. Compared with the RBF neural network, a combination of the theory model and SVM forecasting model improves the accuracy of steel strip thickness abundantly.",
keywords = "HAGC, Rolling gap, SVM Regression, Steel strip thickness",
author = "Wei Li and Xiaolan Yao and Lei Yu and Yue Guo",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 27th Chinese Control and Decision Conference, CCDC 2015 ; Conference date: 23-05-2015 Through 25-05-2015",
year = "2015",
month = jul,
day = "17",
doi = "10.1109/CCDC.2015.7162527",
language = "English",
series = "Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3490--3494",
booktitle = "Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015",
address = "United States",
}