偏最小二乘回归
主成分分析
模式识别(心理学)
非线性系统
计算机科学
人工神经网络
人工智能
过程(计算)
差异(会计)
组分(热力学)
成分分析
特征提取
相关系数
主成分回归
数据挖掘
生物系统
机器学习
物理
会计
业务
操作系统
热力学
生物
量子力学
作者
Yonghui Wang,Zhijiang Lou
标识
DOI:10.1109/safeprocess52771.2021.9693603
摘要
To handle the nonlinear feature in the industry process, this paper combines partial least squares (PLS) and neural component analysis (NCA), named as NCA-PLS. Different from NCA, the principal components are selected based on the correlation coefficient with KPI variables rather than the variance. As such, by redesigning the PCs extraction mechanism, NCA-PLS can successfully extract the KPI-related components from the process data and use them for process monitoring.
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