Reinforcement Aided Latent Temporal Feature Transfer Learning: Time Series Prediction With Insufficient Labeled Data for Industrial Chemical Process

Reinforcement Aided Latent Temporal Feature Transfer Learning: Time Series Prediction With Insufficient Labeled Data for Industrial Chemical Process

Time series data of industrial chemical process are typically collected in two ways: distributed control system (DCS) and laboratory test. With the popularity of DCS, a large amount of industrial chemical process monitoring data can be collected (label-sufficient), which is commonly feature rich but...

Full description

Saved in:
Bibliographic Details
Main Authors: Han Jiang, Wenyu Yang, Zhibin Sun, Shucai Zhang, Jingru Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Transfer learning
reinforcement learning
latent features
time series prediction
NOx concentration
Online Access:https://ieeexplore.ieee.org/document/10745485/
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items