Transactions of Materials and Heat Treatment

Title：Rolling force prediction of rolling mill based on improved support vector machine algorithm

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ClassificationCode：TP273

year,vol(issue):pagenumber：2019,44(4):131-137

Abstract：

Considering that the prediction model based on neural network algorithm has better prediction accuracy, but the neural network model needs a large number of training samples which increases the complexity of the model, a rolling force prediction model of rolling mill based on improved support vector machine was studied. Then, the least squares support vector machine based on RBF kernel function and polynomial kernel function was established, and the parameters of hybrid function were optimized by the cooperative quantum particle swarm optimization algorithm to improve the prediction performance of the prediction model. Furthermore, the RBF kernel function parameter γ, penalty coefficient c, polynomial kernel function parameter q and weight a of two kernel functions in the rolling force prediction model of rolling mill based on the improved support vector machine were obtained by the cooperative quantum particle swarm optimization algorithm. The example study show that the relative error of rolling force prediction model based on the improved support vector machine is between 4%-6%, and the average error of multi-group data is 4.83%. Thus the feasibility of the rolling force prediction model based on the improved support vector machine is verified. The prediction model studied in this paper takes longer time than the other three kinds of comparison models, but it is more practical to improve the prediction accuracy.

Funds：

河南省自然科学基金资助项目（163400510331）

王前锋（1981-），男，硕士，讲师 E-mail：wangqianfengpaper@126.com

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