人工智能
核(代数)
计算机科学
特征提取
特征(语言学)
利用
模式识别(心理学)
深度学习
偏最小二乘回归
前馈
机器学习
工程类
数学
控制工程
语言学
哲学
计算机安全
组合数学
作者
Yongxuan Chen,Xiaogang Deng
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:19 (3): 3178-3187
被引量:4
标识
DOI:10.1109/tii.2022.3182023
摘要
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.
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