基于改进聚类和加权bagging的多模型软测量建模

doi:10.3969/j.issn.0438-1157.2012.09.004

化工学报 ›› 2012, Vol. 63 ›› Issue (9): 2697-2702.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

张文清, 傅雨佳, 杨慧中

江南大学教育部轻工过程先进控制重点实验室, 江苏无锡 214122

收稿日期:2012-06-14 修回日期:2012-06-20 出版日期:2012-09-05 发布日期:2012-09-05
通讯作者: 杨慧中
作者简介:张文清(1987-),男,硕士研究生。
基金资助:
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)。

Multi-model soft-sensor modeling based on improved clustering and weighted bagging

ZHANG Wenqing, FU Yujia, YANG Huizhong

Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2012-06-14 Revised:2012-06-20 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,the 111 Project(B12018)and the Doctor Candidate Foundation of Jiangnan University(JUDCF12030).

摘要/Abstract

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。

关键词: K-近邻, 多模型, 集成学习, bagging, 支持向量机

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.

Key words: K-neighbors, multi-model, ensemble learning, bagging, support vector machine

中图分类号:

TP393

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.

ZHANG Wenqing, FU Yujia, YANG Huizhong. Multi-model soft-sensor modeling based on improved clustering and weighted bagging[J]. CIESC Journal, 2012, 63(9): 2697-2702.

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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

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