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Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging

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Lu Wu 1   Mao Zhizhong 2   Yuan Ping 1  
文摘 For accurately forecasting the liquid steel temperature in ladle furnace (LF), a novel temperature prediction model based on optimally pruned Bagging combined with modified extreme learning machine (ELM) is proposed. By analyzing the mechanism of LF thermal system, a thermal model with partial linear structure is obtained. Subsequently, modified ELM, named as partial linear extreme learning machine (PLELM), is developed to estimate the unknown coefficients and undefined function of the thermal model. Finally, a pruning Bagging method is proposed to establish the aggregated prediction model for the sake of overcoming the limitation of individual predictor and further improving the prediction performance. In the pruning procedure, AdaBoost is adopted to modify the aggregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggregation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging's robustness against highly influential points, reduce the storage needs as well as speed up the computing time. The proposed prediction model is examined by practical data, and comparisons with other methods demonstrate that the new ensemble predictor can improve prediction accuracy, and is usually consisted compactly.
来源 Journal of Iron and Steel Research , International,2012,19(12):21-28 【核心库】
DOI 10.1016/s1006-706x(13)60027-8
关键词 Bagging ; extreme learning machine ; LF liquid steel temperature prediction model ; AdaBoost
地址

1. College of Information Science and Engineering, Northeastern University, Liaoning, Shenyang, 110004  

2. College of Information Science and Engineering, Northeastern University, State Key Laboratory of Synthetical Automation, Liaoning, Shenyang, 110004

语种 英文
ISSN 1006-706X
学科 冶金工业
基金 Item Sponsored by Fundamental Research Funds for Central Universities of China ;  国家自然科学基金
文献收藏号 CSCD:4725263

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引证文献 1 篇

1 Feng Kai Endpoint temperature prediction of molten steel in RH using improved case-based reasoning International Journal of Minerals, Metallurgy and Materials,2013,20(12):1148-1154
被引 3 次

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