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摘要
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.
Note to Practitioners-In practical industrial process, there are many important parameters that cannot be calculated accurately. Many intelligent methods have been used to estimate these parameters based on production data in the past decades. However, sometimes, the accuracy of estimation is not satisfied for industrial production. In this study, a new ensemble ELM is proposed using modified AdaBoost.RT as a efficiently intelligent method. It is also used to establish an intelligent model for predicting the temperature of molten steel in LF as an example, and the good performance of the new ensemble is demonstrated. The new proposed method can boost the accuracy of the method that using single intelligent algorithm. Therefore, it can be widely used in intelligent prediction or estimation especially for the important industrial parameters.
