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A Deep Supervised Learning Framework Based on Kernel Partial Least Squares for Industrial Soft Sensing
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DOI：10.1109/TII.2022.3182023.png)
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摘要
En 中文
Kernel partial least squares (KPLS) is a widely used soft sensor modeling method for nonlinear industrial processes. However, the traditional KPLS is considered as the shallow learning machine and may not capture the vital information hidden among data. In order to exploit the intrinsic data feature information, in this article, we propose a deep supervised learning framework based on KPLS, which is referred to as deep KPLS (DeKPLS). First, inspired by the deep learning mechanism, a hierarchical feature extraction framework based on KPLS is proposed, where the KPLS is served as the basic feature extraction module. Then, a layer-wise feedforward training strategy is designed for the determination of model architecture. Finally, two actual industrial processes are utilized to demonstrate the effectiveness of the proposed DeKPLS.
Keyword:
Soft sensors
Kernel
Feature extraction
Deep learning
Training
Data models
Testing
kernel partial least squares (KPLS)
quality prediction
soft sensor
supervised learning
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering.png)
IEEE ACCESS
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IEEE ACCESSIF3.6
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