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| Research on Cold Rolling Force Prediction Model Based on Improved Deep Belief Network |
| WEI Li-xin1,2,WANG Heng1,2,SUN Hao1,2,HU Zi-yu1,2 |
1. Intelligent Control System and Intelligent Equipment Engineering Research Center of Ministry of Education,Yanshan University, Qinhuangdao, Hebei 066004, China
2. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract In the cold rolling process of strip steel, the accuracy of rolling force prediction directly determines the rolling precision and product quality of the strip. The traditional single-hidden layer-based neural network modeling method is simple in structure, and the expression ability and generalization ability of complex functions are restricted. The rolling site environment is complex, and data measurement has noise interference,which will directly affect the forecasting accuracy. Regarding the issue above, an improved deep belief network prediction model based on unsupervised learning is proposed. The construction of denoising-restricted Boltzmann machines and deep networks can improve the systems ability to learn the characteristics of input data, while training the deep network with improved contrast divergence algorithm.Finally,the model is tested by using the measured data of a steel mills 1200mm rolling mill, and three different models are compared and analyzed. The results show that the average relative error of the rolling force prediction of the model is controlled within 4.5%, and the time required for modeling is reduced by 26% compared to the self-encoding network.
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Received: 18 November 2019
Published: 15 July 2021
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2021, Vol. 42 
