化工学报2019，Vol.70Issue(2)：475-480,6.DOI:10.11949/j.issn.0438⁃1157.20181355
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
摘要
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
极限学习机/神经网络/主元分析/过程建模/化工生产/过程控制Key words
extreme learning machine/ neural network/ principal components analysis/ processes modeling/chemical production/ process control分类
信息技术与安全科学引用本文复制引用
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):475-480,6.基金项目
国家自然科学基金青年项目(61703027) （61703027）
国家自然科学基金重点项目(61533003) （61533003）
中央高校基本科研业务费专项资金(JD1808,XK1802-4) （JD1808,XK1802-4）
