Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning-Reference-Cited by-同舟云学术

Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning

Published:2022-09 Issue:9 Volume:23 Page:15155-15165
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Yu Hang¹^ORCID,Lu Jie¹^ORCID,Liu Anjin¹,Wang Bin¹,Li Ruimin²,Zhang Guangquan¹^ORCID

Affiliation:

1. Decision Systems and e-Service Intelligence Laboratory, Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia

2. Operations Analysis and Modeling Department, Sydney Trains, Haymarket, NSW, Australia

Funder

Carriage Load Prediction Project coordinated by Sydney Trains

Australian Research Council (ARC) under Discovery

Laureate Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9893028/09678130.pdf?arnumber=9678130

Reference51 articles.

1. Short-Term Traffic Flow Prediction Based on Combination Model of Xgboost-Lightgbm

2. Robust Weighted Sum-Rate Maximization for the Multi-Stream MIMO Interference Channel With Sparse Equalization

3. Topology learning-based fuzzy random neural network for streaming data Regression;yu;IEEE Trans Fuzzy Syst,2020

4. A survey on multi-output regression

5. A linear regression model using triangular fuzzy number coefficients

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