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

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	Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging
其他题名	Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging
	Lue Wu 1; Mao Zhizhong 1; Yuan Ping 1
	2012
发表期刊	JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL
ISSN	1006-706X
卷号	19 期号:12 页码:21-28
摘要	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.
其他摘要	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.
关键词	ENSEMBLES NETWORKS Bagging extreme learning machine LF liquid steel temperature prediction model AdaBoost
收录类别	CSCD
语种	英语
资助项目	[Fundamental Research Funds for Central Universities of China] ; [National Natural Science Foundation of China]
CSCD记录号	CSCD:4725263
引用统计	被引频次：5[CSCD] [CSCD记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/149345
专题	中国科学院金属研究所
作者单位	1.东北大学 2.中国科学院金属研究所
推荐引用方式 GB/T 7714	Lue Wu,Mao Zhizhong,Yuan Ping. Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging[J]. JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL,2012,19(12):21-28.
APA	Lue Wu,Mao Zhizhong,&Yuan Ping.(2012).Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging.JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL,19(12),21-28.
MLA	Lue Wu,et al."Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging".JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL 19.12(2012):21-28.