摘要
For accurately forecasting the liquid steel temperature in ladle furnace （LF）, a novel temperature predic tion model based on optimally pruned Bagging combined with modified extreme learning machine （ELM） is pro posed. 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 pro- posed 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 ag- gregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggre- gation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging＇s ro- bustness against highly influential points, reduce the storage needs as well as speed up the computing time. The pro- posed 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.
For accurately forecasting the liquid steel temperature in ladle furnace （LF）, a novel temperature predic tion model based on optimally pruned Bagging combined with modified extreme learning machine （ELM） is pro posed. 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 pro- posed 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 ag- gregation order of the original Bagging ensembles, and a novel early stopping rule is designed to terminate the aggre- gation earlier. As a result, an optimal pruned Bagging ensemble is achieved, which is able to retain Bagging＇s ro- bustness against highly influential points, reduce the storage needs as well as speed up the computing time. The pro- posed 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.
基金
Sponsored by Fundamental Research Funds for Central Universities of China(110604011,110304006)
National Natural Science Foundation of China(61074098)
