Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderl...

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Bibliographic Details
Main Authors: Xiaogang Deng, Yongxuan Chen, Ping Wang, Yuping Cao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Soft sensor
nonlinearity
unobserved multimode
kernel partial least squares
latent factor clustering
Online Access:https://ieeexplore.ieee.org/document/9001123/

Internet

https://ieeexplore.ieee.org/document/9001123/

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