Locally weighted slow feature regression for nonlinear dynamic soft sensor modeling and its application to an industrial hydrocracking process-Reference-Cited by-同舟云学术

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Locally weighted slow feature regression for nonlinear dynamic soft sensor modeling and its application to an industrial hydrocracking process

Published:2020-02-03 Issue:5 Volume:31 Page:055101
ISSN:0957-0233
Container-title:Measurement Science and Technology
language:
Short-container-title:Meas. Sci. Technol.

Author:

Yuan Xiaofeng^ORCID,Zhou Jiao,Wang Yalin

Funder

Innovation-driven plan in Central South University

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Hunan Postgraduate Research Innovation Project

Publisher

IOP Publishing

Subject

Applied Mathematics,Instrumentation,Engineering (miscellaneous)

Link

https://iopscience.iop.org/article/10.1088/1361-6501/ab5f1b/pdf

Reference41 articles.

1. Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

2. Grinding process monitoring based on electromechanical impedance measurements

3. Review of Recent Research on Data-Based Process Monitoring

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1. Virtual Sensing Techniques for Nonstationary Processes Based on a Multirate Probabilistic Dual-Latent-Variable Supervised Slow Feature Analysis;IEEE Transactions on Industrial Informatics;2024

2. No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models;IEEE Transactions on Automation Science and Engineering;2023-01

3. VAE-Based Interpretable Latent Variable Model for Process Monitoring;IEEE Transactions on Neural Networks and Learning Systems;2023

4. Slow Down to Go Better: A Survey on Slow Feature Analysis;IEEE Transactions on Neural Networks and Learning Systems;2022

5. Mixed kernel principal component weighted regression based on just-in-time learning for soft sensor modeling;Measurement Science and Technology;2021-10-25

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