A Priori Knowledge-Based Dual Hierarchical RNN for Spatial–Temporal Process Modeling: Using a Multitubular Reactor as a Case Study-Reference-Cited by-同舟云学术

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A Priori Knowledge-Based Dual Hierarchical RNN for Spatial–Temporal Process Modeling: Using a Multitubular Reactor as a Case Study

Published:2024-01 Issue:1 Volume:20 Page:899-910
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Sun Pei¹^ORCID,Chen Junghui²^ORCID,Que Haoyi³^ORCID

Affiliation:

1. SAIC Motor Innovation R&D Headquarters, Shanghai, China

2. Department of Chemical Engineering, Chung-Yuan Christian University, Taoyuan, Taiwan

3. School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, China

Funder

Ministry of Science and Technology, Taiwan

National Science and Technology Council, Taiwan, ROC

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10355676/10113650.pdf?arnumber=10113650

Reference28 articles.

1. Dynamic Spatial-Independent-Component-Analysis-Based Abnormality Localization for Distributed Parameter Systems

2. Spectral Methods and Their Applications

3. Spatial Construction for Modeling of Unknown Distributed Parameter Systems

4. Low-order control-relevant models for a class of distributed parameter systems

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