Skip to content
RDL
Network
Ekosistem
Uygulama değiştir
EN
Hakkımızda
SSS
Giriş yap
Başla
A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning — Hongyue Guo (2025) | RDL Network
Back
Cite
Save
Save for later
Share
Home
Publications
A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning
Shared by
Witold Pedrycz
University of Alberta
A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning
Article
2025
en
Authors
+3 more
HG
Hongyue Guo
YZ
Yijia Zhang
YY
Yating Yu
Discussion
(0)
Sign in
to like and join the discussion.
No comments yet. Be the first to comment.
Related publications
Article
2020
Operating Mode Recognition Based on Fluctuation Interval Prediction for Iron Ore Sintering Process
Sheng Du
,
Min Wu
,
Luefeng Chen
,
Jie Hu
,
Li Jin
,
Weihua Cao
,
Witold Pedrycz
Article
2021
Prediction model of burn-through point with fuzzy time series for iron ore sintering process
Sheng Du
,
Min Wu
,
Luefeng Chen
,
Witold Pedrycz
Article
2019
Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process
Jie Hu
,
Min Wu
,
Xin Chen
,
Weihua Cao
,
Witold Pedrycz
Article
2024
Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review
Sheng Du
,
Xian Ma
,
Haipeng Fan
,
Jie Hu
,
Weihua Cao
,
Min Wu
,
Witold Pedrycz
Article
2020
Weighted Kernel Fuzzy C-Means-Based Broad Learning Model for Time-Series Prediction of Carbon Efficiency in Iron Ore Sintering Process
Jie Hu
,
Min Wu
,
Luefeng Chen
,
Kailong Zhou
,
Pan Zhang
,
Witold Pedrycz
Discussion(0)
No comments yet. Be the first to comment.