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Ladle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging
Alternative TitleLadle Furnace Liquid Steel Temperature Prediction Model Based on Optimally Pruned Bagging
Lue Wu1; Mao Zhizhong1; Yuan Ping1
2012
Source PublicationJOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL
ISSN1006-706X
Volume19Issue:12Pages:21-28
AbstractFor 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.
Other AbstractFor 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.
KeywordENSEMBLES NETWORKS Bagging extreme learning machine LF liquid steel temperature prediction model AdaBoost
Indexed ByCSCD
Language英语
Funding Project[Fundamental Research Funds for Central Universities of China] ; [National Natural Science Foundation of China]
CSCD IDCSCD:4725263
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/149345
Collection中国科学院金属研究所
Affiliation1.东北大学
2.中国科学院金属研究所
Recommended Citation
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.
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