Dual Cross-Attention Transformer Networks for Temporal Predictive Modeling of Industrial Process-Reference-Cited by-同舟云学术

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Dual Cross-Attention Transformer Networks for Temporal Predictive Modeling of Industrial Process

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wang Jie¹^ORCID,Xie Yongfang¹^ORCID,Xie Shiwen¹^ORCID,Chen Xiaofang¹^ORCID

Affiliation:

1. School of Automation, Central South University, Changsha, China

Funder

State Key Program of National Natural Science of China

Distinguished Youth Foundation of Hunan Nature Science Foundation

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10494324.pdf?arnumber=10494324

Reference38 articles.

1. Adversarial Training-Based Deep Layer-Wise Probabilistic Network for Enhancing Soft Sensor Modeling of Industrial Processes

2. Unsupervised heat balance indicator construction based on variational autoencoder and its application to aluminum electrolysis process monitoring

3. Tuning of fuzzy controller with arbitrary triangular input fuzzy sets based on proximal policy optimization for time-delays system

4. Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling

5. Fractional stochastic configuration networks-based nonstationary time series prediction and confidence interval estimation

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