基于主元提取的鲁棒极限学习机研究及其化工建模应用

张晓晗; 汪平江; 顾祥柏; 徐圆; 贺彦林; 朱群雄

doi:10.11949/j.issn.0438-1157.20181355

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基于主元提取的鲁棒极限学习机研究及其化工建模应用

过程系统工程 | 更新时间：2026-07-24

- 基于主元提取的鲁棒极限学习机研究及其化工建模应用
- Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes
- 化工学报 2019年70卷第2期页码：475-480
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京100029
  2.智能过程系统工程教育部工程研究中心，北京100029
  3.中石化炼化工程(集团)股份有限公司，北京100101
- 作者简介：
  
  张晓晗（1995—），女，博士研究生，xiao.han_zhang@qq.com
  贺彦林（1987—），男，博士，副教授，heyl@mail.buct.edu.cn
  朱群雄（1960—），男，博士，教授，zhuqx@mail.buct.edu.cn
- 基金信息：
  
  国家自然科学基金青年项目(61703027);国家自然科学基金重点项目(61533003);中央高校基本科研业务费专项资金(JD1808;XK1802-4)
- DOI：10.11949/j.issn.0438-1157.20181355
  中图分类号： TP 29
- 收稿：2018-11-16，
  
  修回：2018-11-26，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
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张晓晗, 汪平江, 顾祥柏, 徐圆, 贺彦林, 朱群雄. 基于主元提取的鲁棒极限学习机研究及其化工建模应用[J]. 化工学报, 2019, 70(2): 475-480

ZHANG Xiaohan, WANG Pingjiang, GU Xiangbai, XU Yuan, HE Yanlin, ZHU Qunxiong. Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes[J]. CIESC Journal, 2019, 70(2): 475-480
张晓晗, 汪平江, 顾祥柏, 徐圆, 贺彦林, 朱群雄. 基于主元提取的鲁棒极限学习机研究及其化工建模应用[J]. 化工学报, 2019, 70(2): 475-480 DOI： 10.11949/j.issn.0438-1157.20181355.

ZHANG Xiaohan, WANG Pingjiang, GU Xiangbai, XU Yuan, HE Yanlin, ZHU Qunxiong. Research on principal components extraction based robust extreme learning machine(PCE-RELM) and its application to modeling chemical processes[J]. CIESC Journal, 2019, 70(2): 475-480 DOI： 10.11949/j.issn.0438-1157.20181355.

摘要

化工生产过程日益复杂，传统极限学习机(extreme learning machine

ELM)无法有效地对化工过程数据建模。针对该问题，提出一种基于主元提取(principal components extraction

PCE)的鲁棒极限学习机(PCE-RELM)。通过对ELM隐含层进行主元分析，提取数据的主元特征，去除变量间的线性相关性，简化研究问题。可以减小隐含层节点数对模型精度的影响，实现对ELM隐含层节点数的快速随机选取，同时使ELM具有鲁棒性。为验证提出方法的有效性，将PCE-RELM模型应用于精对苯二甲酸(purified terephthalic acid

PTA)生产过程建模。仿真结果显示，相比传统的ELM，PCE-RELM模型具有设计简单、鲁棒性好、精度高等优势，可以对化工过程控制、分析起到指导作用。

Abstract

The chemical production processes are increasingly complex and the traditional extreme learning machine (ELM) cannot effectively model the chemical processes data. To tackle this problem

a novel PCE-RELM model based on principal components extraction (PCE) is proposed. Through principal component analysis of the ELM hidden layer

the principal component features of the data are extracted

the linear correlation between variables is removed

and the research problem is simplified. The influence of the number of hidden layer nodes on the accuracy of the model can be reduced

the number of hidden layer nodes in the ELM can be quickly and randomly selected

and the ELM model becomes robust. To verify the effectiveness of the proposed method

the PCE-RELM model was applied to modeling the purified terephthalic acid (PTA) production process. The simulation results show that

compared with the traditional ELM

the PCE-RELM model has the advantages of simple design

good robustness and high accuracy

which can guide the chemical process control and analysis.

关键词

Keywords

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