[1]LIU Minghua,ZHANG Qiang.Prediction of strip crown based on support vector machine and neural network[J].CAAI Transactions on Intelligent Systems,2022,17(3):506-514.[doi:10.11992/tis.202101002]
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Prediction of strip crown based on support vector machine and neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 3 Page number: 506-514 Column: 学术论文—机器学习 Public date: 2022-05-05
Title:
Prediction of strip crown based on support vector machine and neural network
Author(s):
LIU Minghua;  ZHANG Qiang
School of Metallurgical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
Keywords:
support vector regression; neural network; strip crown; particle swarm optimization algorithm; hot strip rolling process; machine learning; prediction; big data
CLC:
TP16
DOI:
10.11992/tis.202101002
Abstract:
To improve the prediction accuracy of strip crown in a hot-rolled production process, a modeling method combining particle swarm optimization (PSO) algorithm, support vector regression (SVR), and BP neural network (BPNN) is proposed. The PSO algorithm was employed to optimize the parameters of the SVR model, and the PSO–SVR strip crown prediction model was established. A method combining the BPNN strip crown deviation model with the PSO–SVR strip crown model is also proposed to predict the strip crown. The prediction accuracy of the model was validated with field experimental data, and the comprehensive performance of the model was evaluated using statistical indicators. The simulation results show that in contrast to the PSO–SVR, SVR, BPNN, and GA-SVR models, the PSO–SVR+BPNN model exhibits a greater learning capacity and generalization ability. The computing time of the PSO–SVR+BPNN model is also less than that of the GA-SVR model.
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