摘要
针对化工生产过程中软测量模型估计精度的问题,提出一种基于改进聚类和加权bagging的多模型建模方法。该方法在传统FCM聚类的基础上,利用K-近邻处理进一步降低错分率,改善聚类效果;通过相关性分析对训练样本集进行特征分组,将原始集划分为多个特征集;最后根据加权bagging的集成学习算法,融合支持向量机自适应地实现多模型建模。仿真结果表明,该建模方法可以合理地加权分配特征子模型,使得模型估计精度得到提高,具有更强的泛化能力。
As for the problem that the estimation precision of soft sensor model is not enough on line in chemical processing,a method of multi-model soft sensor is proposed based on improved clustering and weighted bagging.It improves clustering result by reducing error dividing probability with K-neighbors based on traditional fuzzy C-means clustering,and the training sample set is grouped into several feature sets with correlation analysis.At last,a multi-model is constructed by support vector machines adaptively according to weighted bagging algorithm of ensemble learning.The simulation results show that every feature model is assigned with weight reasonably,and the estimated accuracy of model is improved,and the generalization ability is better.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2012年第9期2697-2702,共6页
CIESC Journal
基金
江苏高校优势学科建设工程资助项目
高等学校学科创新引智计划项目(B12018)
江南大学博士研究生科学研究基金项目(JUDCF12030)~~
