文献类型：期刊文献

中文题名：基于正交独立成分分析的过程数据建模

英文题名：Process Data Modeling Based on Orthogonal Independent Component Analysis

作者：罗明英[1];侍洪波[1];谭帅[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2016

卷号：45

期号：5

起止页码：551

中文期刊名：信息与控制

外文期刊名：Information and Control

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金资助项目(61374140)

语种：中文

中文关键词：质量预测;非高斯过程;正交信号校正;独立成分分析

外文关键词：quality prediction; non-Gaussian process; orthogonal signal correction; independent component analysis

摘要：针对非高斯数据分布过程中回归预测精度不足的问题,提出一种在独立成分分析(ICA)的基础上与正交信号校正(OSC)相结合的多元线性回归(MLR)方法——正交独立成分回归(O-ICR).首先将原输入数据通过正交ICA(O-ICA)进行预处理,去除ICA在提取高阶统计量时带来的与Y无关的干扰变化,然后对校正后的X提取独立成分,代替原输入数据建立与Y之间的回归预测模型.与传统的ICR相比,该方法提取的独立成分经过校正可使回归模型的预测精度更高.最后通过Tennessee Eastman(TE)过程的质量预测仿真,验证了该建模方法的有效性.
Based on independent component analysis（ICA）,a multivariate linear regression（MLR） method combined with orthogonal signal correction（OSC）,which is called orthogonal independent component regression（O-ICR）,is proposed for regression prediction of non-Gaussian processes. First,the O-ICA is conducted on an original input data matrix for removing disturbing variation that is not correlated to Y from the extracted high-order statistics in ICA. Then,independent components are extracted X from after correction. The regression prediction model is derived using these components instead of the original input data and Y. Compared with the traditional ICR,the proposed method has a more superior performance because independent components are corrected. Finally,the validity of the method is verified though quality prediction simulation in the Tennessee Eastman（TE） process.