基于增量学习思想的改进AdaBoost建模方法  

Improved AdaBoost modeling method based on incremental learning

作　　者：田慧欣[1] 王安娜[2] 

机构地区：[1]天津工业大学电气工程与自动化学院,天津300387 [2]东北大学信息科学与工程学院,沈阳110819

出　　处：《控制与决策》2012年第9期1433-1436,共4页Control and Decision

基　　金：国家自然科学基金项目(60843007);天津市应用基础及前沿技术研究计划项目(11JCYBJC07000)

摘　　要：针对软测量建模的特点以及建模过程中存在的主要问题,提出了基于AdaBoost RT集成学习方法的软测量建模方法,并根据AdaBoost RT算法固有的不足和软测量模型在线更新所面临的困难,提出了自适应修改阈值φ和增添增量学习性能的改进方法,使用该建模方法对宝钢300t LF精炼炉建立钢水温度软测量模型,并使用实际生产数据对模型进行了检验,检验结果表明,该模型具有较好的预测精度,能够很好地实现在线更新。Aiming at the characters and problems of soft sensor, a soft sensor modeling method based on ensemble learning algorithm AdaBoost RT is presented. According to the shortages of AdaBoost RT and the difficulties of updating for soft sensor model, the self-adaptive modified value of φ and the incremental learning character added improved methods are proposed. The method is used to establish the soft sensor model of molten steel temperature in 300 t LE The product data are used to test the model. The test results show that the proposed soft sensor model based on improved AdaBoost RT can improve the prediction accuracy and update real time.

关 键 词：软测量 ADABOOST 增量学习 极限学习机 精炼炉 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]