A new soft sensor modeling method based on modified AdaBoost with incremental learning

Aiming at the characteristics of soft sensors, an ensemble learning algorithm AdaBoost.RT is used to establish the soft sensor models. According to the shortcoming of AdaBoost.RT and the difficulties of on-line updating for soft sensor models, a self-adaptive modifying threshold ¿ and an incremental learning method are proposed for improving the performance of original AdaBoost.RT. The new modified AdaBoost.RT can overcome the disadvantages of original AdaBoost.RT and update the soft sensor model in real time. The new method is used to establish the soft sensor model of molten steel temperature in 300t LF. Practical production data are used to test the model. The results demonstrate that the new soft sensor model based on modified AdaBoost.RT can improve the prediction accuracy and has good ability of update.