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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Prediction of Rolling Force Based on a Fusion of Extreme Learning Machine and Self Learning Model of Rolling Force
verfasst von : AZiGuLi, Can Cui, Yonghong Xie, Shuang Ha, Xiaochen Wang
Erschienen in: Advances in Intelligent Systems and Interactive Applications
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Abstract
Aiming at the rolling force model of hot strip rolling mill, the forecasting technique based on ELM (extreme learning machine) is presented in this paper. Initially, the variables associated with control rolling are identified by the analysis of the traditional formula of rolling force, in order to ensure the effectiveness of the model, and then apply ELM network to predict models. Production data is applied to train and test the above network, and compare with the modified calculated value of rolling force, which got from the self-learning model of rolling force. The results show that the thickness can be predicted more rapidly and exactly, which can meet the actual demand of production, when this model and the rolling force learning model are integrated.