New Feature Selection Method and Its Applications on Prediction of Hot Metal Silicon Content in Blast Furnace-Reference-Cited by-同舟云学术

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New Feature Selection Method and Its Applications on Prediction of Hot Metal Silicon Content in Blast Furnace

Published:2023-02 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)
language:
Short-container-title:

Author:

Yin Linzi¹,Wang Yutai¹,Cheng Pan¹,Guan Yuyin¹

Affiliation:

1. Central South University,School of Physics and Electronics,Changsha,China,410012

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10145214/10145216/10145260.pdf?arnumber=10145260

Reference13 articles.

1. Feature Selection Algorithm Based on Label Correlation;l?;Patt Recog Artif Intell,2020

2. An efficient attribute reduction algorithm using MapReduce [J];yin;Journal of Information Science,2019

3. Prediction of silicon content in hot metal using neural network and rough set theory[J];yin;Journal of Iron and Steel Research,2019

4. Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

5. Application of improved EMD-Elman neural network to predict silicon content in hot metal[J];song;CIESC Journal,2016

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