Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace. Issue 3 (August 2016)

• Record Type: Journal Article
• Authors: Wang, Xiaojun; You, Mingshuang; Mao, Zhizhong; Yuan, Ping
• Is Part Of: Advanced engineering informatics. Volume 30:Issue 3(2016:Aug.)
• Page Start: 368
• Page End: 375
• Publication Date: 2016-08
• Subjects: Ladle Furnace -- Temperature prediction model -- Ensemble method -- General Regression Neural Networks; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2016.05.001
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: To control the molten steel temperature in a Ladle Furnace accurately, it is necessary to build a precise (i.e. accurate and good generalized) temperature prediction model. To solve this problem, ensemble modeling methods have been applied to predict the temperature. Now, in the production process, large-scale data with more helpful information are sampled, which provides possibilities to improve the precision of the temperature prediction. Although most of the existing ensemble temperature models have strong learning ability, they are not suitable for the large-scale data. In this paper, to solve the large-scale issue, the Tree-Structure Ensemble General Regression Neural Networks (TSE-GRNNs) method is proposed. Firstly, small-scale sample subsets are constructed based on the regression tree algorithm. Secondly, GRNN sub-models are built on sample subsets, which can be designed very quickly and cannot converge to poor solutions according to local minima of the error criterion. Then, the TSE-GRNNs method is applied to establish a temperature model. Experiments show that the TSE-GRNNs temperature model is more precise than the other existing temperature models, and meets the requirements of the RMSE and the maximum error of the molten steel temperature prediction in Ladle Furnace.