主成分分析
软传感器
主成分回归
核(代数)
非线性系统
计算机科学
核主成分分析
组分(热力学)
过程(计算)
生物系统
回归
人工智能
数据挖掘
模式识别(心理学)
算法
数学
统计
支持向量机
核方法
物理
组合数学
操作系统
热力学
生物
量子力学
作者
Xiaofeng Yuan,Zhiqiang Ge
摘要
The principal component regression (PCR) based soft sensor modeling technique has been widely used for process quality prediction in the last decades. While most industrial processes are characterized with nonlinearity and time variance, the global linear PCR model is no longer applicable. Thus, its nonlinear and adaptive forms should be adopted. In this paper, a just-in-time learning (JITL) based locally weighted kernel principal component regression (LWKPCR) is proposed to solve the nonlinear and time-variant problems of the process. Soft sensing performance of the proposed method is validated on an industrial debutanizer column and a simulated fermentation process. Compared to the JITL-based PCR, KPCR, and LWPCR soft sensing approaches, the root-mean-square errors (RMSE) of JITL-based LWKPCR are the smallest and the prediction results match the best with the actual outputs, which indicates that the proposed method is more effective for quality prediction in nonlinear time-variant processes.
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