基于k-近邻互信息的发酵过程高斯过程回归建模

赵荣荣; 赵忠盖; 刘飞

doi:10.11949/0438-1157.20190606

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基于k-近邻互信息的发酵过程高斯过程回归建模

过程系统工程 | 更新时间：2026-07-24

- 基于k-近邻互信息的发酵过程高斯过程回归建模
- Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information
- 化工学报 2019年70卷第12期页码：4741-4748
- 作者机构：
  
  江南大学轻工过程先进控制教育部重点实验室，江苏无锡 214122
- 作者简介：
  
  赵荣荣（1993—），女，硕士研究生，1252082423@qq.com
  赵忠盖（1976—），男，博士，教授，gaizihao@jiangnan.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61573169)
- DOI：10.11949/0438-1157.20190606
  中图分类号： TP 273
- 收稿：2019-05-31，
  
  修回：2019-08-30，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
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赵荣荣, 赵忠盖, 刘飞. 基于k-近邻互信息的发酵过程高斯过程回归建模[J]. 化工学报, 2019, 70(12): 4741-4748

ZHAO Rongrong, ZHAO Zhonggai, LIU Fei. Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information[J]. CIESC Journal, 2019, 70(12): 4741-4748
赵荣荣, 赵忠盖, 刘飞. 基于k-近邻互信息的发酵过程高斯过程回归建模[J]. 化工学报, 2019, 70(12): 4741-4748 DOI： 10.11949/0438-1157.20190606.

ZHAO Rongrong, ZHAO Zhonggai, LIU Fei. Gaussian process regression modeling of fermentation process based on k-nearest neighbor mutual information[J]. CIESC Journal, 2019, 70(12): 4741-4748 DOI： 10.11949/0438-1157.20190606.

摘要

发酵过程中基质浓度往往无法在线测量，采用高斯过程回归（GPR）建立基质浓度的估计模型，实现了其软测量。不同于传统软测量方法对基质浓度的估计，该方法不仅可以得到估计值，还能够得到其估计方差。考虑到发酵过程中各变量之间的非线性、相关性，为了提高模型的预测性能，在模型建立之前首先用k-近邻互信息（k-MI）辅助变量选择方法对模型的输入变量进行选择。从青霉素发酵过程的应用结果来看，采用kMI-GPR方法取得了较好的估计效果。

Abstract

The concentration of the substrate during fermentation is often not measured online. In this paper

Gaussian process regression (GPR) is used to establish an estimation model of substrate concentration

and its soft measurement is realized. Different from traditional regression models

the GPR model can not only predict the quality value

but also provide the estimation variance. In order to improve the prediction performance of the model in the nonlinear fermentation process with correlated variables

the input variables of the model are selected by the k-nearest neighbor mutual information (k-MI) method before the model development. The application results of penicillin fermentation process show the ideal prediction performance based on the kMI-GPR model.

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references

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