化工学报2012，Vol.63Issue(9)：2697-2702,6.DOI:10.3969/j.issn.0438-1157.2012.09.004
基于改进聚类和加权bagging的多模型软测量建模
Multi-model soft-sensor modeling based on improved clustering and weighted bagging
摘要
Abstract
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.关键词
K-近邻/多模型/集成学习/bagging/支持向量机Key words
K-neighbors/ multi-model/ ensemble learning/ bagging/ support vector machine分类
信息技术与安全科学引用本文复制引用
张文清,傅雨佳,杨慧中..基于改进聚类和加权bagging的多模型软测量建模[J].化工学报,2012,63(9):2697-2702,6.基金项目
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) （B12018）
江南大学博士研究生科学研究基金项目(JUDCF12030). （JUDCF12030）
