A Multistep Sequence-to-Sequence Model With Attention LSTM Neural Networks for Industrial Soft Sensor Application-Reference-Cited by-同舟云学术

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A Multistep Sequence-to-Sequence Model With Attention LSTM Neural Networks for Industrial Soft Sensor Application

Published:2023-05-15 Issue:10 Volume:23 Page:10801-10813
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Ma Lianwei¹^ORCID,Zhao Yuan²^ORCID,Wang Bin³^ORCID,Shen Feifan¹^ORCID

Affiliation:

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

2. School of Control Science and Engineering, Zhejiang University, Hangzhou, China

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

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

Key Research and Development Program of Ningbo

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10124342/10102789.pdf?arnumber=10102789

Reference45 articles.

1. Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models

2. Rate-dependent asymmetric hysteresis modeling and robust adaptive trajectory tracking for piezoelectric micropositioning stages

3. Semisupervised learning for probabilistic partial least squares regression model and soft sensor application

4. Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties

5. Deep learning;lecun;Nature,2015

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