Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes-Reference-Cited by-同舟云学术

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Memory-Adaptive Supervised LSTM Networks for Deep Soft Sensor Development of Industrial Processes

Published:2024-07-01 Issue:13 Volume:24 Page:21641-21654
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Shen Feifan¹^ORCID,Wu Jiayang¹,Ye Lingjian²^ORCID,Wang Bin³^ORCID

Affiliation:

1. School of Information Science and Engineering, NingboTech University, Ningbo, China

2. Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou, China

3. Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Zhejiang Provincial Natural Science Foundation of China

Key Research and Development Program of Ningbo

Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10577556/10530548.pdf?arnumber=10530548

Reference43 articles.

1. A Review on Soft Sensors for Monitoring, Control, and Optimization of Industrial Processes

2. Retrofit Self-Optimizing Control: A Step Forward Toward Real Implementation

3. Design of hybrid batch-to-batch and within-batch self-optimizing control structures for batch processes

4. Dynamic mutual information similarity based transient process identification and fault detection

5. Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Efficient Composite Deep Latent Feature Learning Framework With Layerwise Random Mapping for Complicated Industrial Soft Sensor Modeling;IEEE Sensors Journal;2024-09-01

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