An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace

Abstract

Combined the modified AdaBoost.RT with extreme learning machine (ELM), a new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem in this study. First, a new ELM algorithm is selected as ensemble predictor due to its rapid speed and good performance. Second, a modified AdaBoost.RT is proposed to overcome the limitation of original AdaBoost.RT by self-adaptively modifying the threshold value. Then, an ensemble ELM is presented by using the modified AdaBoost.RT for better accuracy of predictability than individual method. Finally, this new hybrid intelligence method is used to establish a temperature prediction model of molten steel by analyzing the metallurgic process of ladle furnace (LF). The model is examined by data of production from 300t LF in Baoshan Iron and Steel Co., Ltd. and compared with the models that established by single ELM, GA-BP (combined genetic algorithm with BP network), and original AdaBoost.RT. The experiments demonstrated that the hybrid intelligence method can improved generalization performance and boost the accuracy, and the accuracy of the temperature prediction is satisfied for the process of practical producing.