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

ZHANG Wenqing; FU Yujia; YANG Huizhong

doi:10.3969/j.issn.0438-1157.2012.09.004

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

更新时间：2025-12-10

- Multi-model soft-sensor modeling based on improved clustering and weighted bagging
- CIESC Journal Issue 9, Pages: 2697-2702(2012)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.004
  CLC： P393
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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ZHANG Wenqing, FU Yujia, YANG Huizhong. Multi-model soft-sensor modeling based on improved clustering and weighted bagging[J]. CIESC Journal, 2012, (9): 2697-2702.
DOI：

ZHANG Wenqing, FU Yujia, YANG Huizhong. Multi-model soft-sensor modeling based on improved clustering and weighted bagging[J]. CIESC Journal, 2012, (9): 2697-2702. DOI： 10.3969/j.issn.0438-1157.2012.09.004.

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。

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.

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