摘要
针对软测量模型在实际应用中遇到的问题,结合Ada Boost集成学习思想,提出适用于软测量回归的集成学习算法,以提高传统软测量模型的精度.为了克服模型更新技术对软测量实际应用的制约,将增量学习机制加入软测量集成建模中,使软测量模型具有在线实时更新的增量学习能力.对浆纱过程使用新方法建立上浆率软测量模型,并使用实际生产数据对模型进行检验,检验结果表明,该模型具有很好的预测精度,并能够较好地实现在线更新.
Aiming at the characters and problems of the soft sensor, a soft sensor modelling method for the soft sensor regression problem based on the ensemble learning algorithm is proposed to improve the accuracy of the soft sensor. According to the shortages of soft sensor update in practical application, an incremental learning idea is added to the proposed ensemble algorithm for soft sensor modelling. The method is used to establish the soft sensor model of sizing in sizing production. The product data is used to test the model. The results show that the proposed soft sensor model can improve the prediction accuracy and realize online update better.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第8期1523-1526,共4页
Control and Decision
基金
国家自然科学基金项目(61403277
61203302)
天津市应用基础与前沿技术研究计划项目(14JCYBJC18900)
关键词
软测量
集成建模
增量学习
极限学习机
上浆率
soft sensor
ensemble modelling
incremental learning
extreme learning machine
sizing
