Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes-Reference-Cited by-同舟云学术

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Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes

Published:2022-05-02 Issue:19 Volume:7 Page:16653-16664
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Zheng Jiaqi¹,Ma Lianwei²,Wu Yi²,Ye Lingjian³^ORCID,Shen Feifan²^ORCID

Affiliation:

1. College of Science & Technology, Ningbo University, Ningbo 315300, People’s Republic of China

2. School of Information Science and Engineering, NingboTech University, Ningbo 315100, People’s Republic of China

3. School of Engineering, Huzhou University, Huzhou 313000, People’s Republic of China

Funder

Ningbo University

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Zhejiang Province

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c01108

Reference42 articles.

1. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

2. Locally weighted kernel partial least squares regression based on sparse nonlinear features for virtual sensing of nonlinear time-varying processes

3. Deep Learning of Latent Variable Models for Industrial Process Monitoring

4. Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data

5. Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

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