A multi-model based soft sensor using evidence theory and Gaussianprocess regression

MEI Congli; YANG Ming; LIU Guohai

doi:10.11949/j.issn.0438-1157.20150492

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A multi-model based soft sensor using evidence theory and Gaussianprocess regression

更新时间：2025-12-10

- A multi-model based soft sensor using evidence theory and Gaussianprocess regression
- CIESC Journal Issue 11, Pages: 4555-4564(2015)
- 作者机构：
  
  江苏大学电气信息工程学院,江苏,镇江,212013
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20150492
  CLC： P13
- Online First：05 November 2015，
  
  Published：05 November 2015
- 稿件说明：
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MEI Congli, YANG Ming, LIU Guohai. A multi-model based soft sensor using evidence theory and Gaussianprocess regression[J]. CIESC Journal, 2015, (11): 4555-4564.
DOI：

MEI Congli, YANG Ming, LIU Guohai. A multi-model based soft sensor using evidence theory and Gaussianprocess regression[J]. CIESC Journal, 2015, (11): 4555-4564. DOI： 10.11949/j.issn.0438-1157.20150492.

摘要

针对生物发酵过程

提出了一种基于证据理论的高斯过程回归多模型软测量方法

其中多模型融合策略同时考虑了数据聚类特性和软测量子模型统计特性。首先

对聚类后的各子类建立高斯过程回归子模型;然后

基于聚类隶属度函数和高斯过程回归子模型后验概率分别设计子模型权值

并利用证据合成规则将两类权值进行证据合成得到融合权值;最后

将该融合权值作为加权因子对子模型进行融合。通过青霉素发酵过程仿真数据和红霉素发酵过程工业数据研究表明

相比单一模型和传统多模型高斯过程回归软测量方法

本文所提方法具有较高的预测精度和较小的预测不确定度。

Abstract

In this paper

a multi-model soft sensor method based on Dempster-Shafer theory (DS) and Gaussian process regression (GPR) was proposed. Firstly

GPR was used to build the sub-models of the proposed soft sensor after clustering training dataset. Secondly

the initial weightings were designed based on membership functions and output posteriori probabilities of GPR based sub-models

respectively. And the initial weightings were fused using the combination rule of DS. Finally

the weighted sum of sub-models with the fused weightings was used to output predictive means and uncertainty. The proposed method was validated on simulation data of a penicillin fermentation process and industrial data of an erythromycin fermentation process. For comparisons

single model-based soft sensor and traditional multi-model soft sensor were also studied. Simulations showed that the proposed method had better predictive accuracy and lower predictive uncertainty.

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