Prediction of Mechanical Properties of Hot Rolled Strips With Generalized RBFNN and Composite Expectile Regression-Reference-Cited by-同舟云学术

Prediction of Mechanical Properties of Hot Rolled Strips With Generalized RBFNN and Composite Expectile Regression

Published:2022 Issue: Volume:10 Page:106534-106542
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

He Xiaoxia¹^ORCID,Zhou Xiaodan¹,Tian Tong²,Li Weigang³

Affiliation:

1. College of Science, Wuhan University of Science and Technology, Wuhan, China

2. College of International Business and Economics, Wuhan Textile University, Wuhan, China

3. Engineering Research Center of Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China

Funder

National Science Foundation of China

Educational Commission of Hubei Province of China

Hubei Province Key Laboratory of Systems Science in Metallurgical Process

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09911639.pdf?arnumber=9911639

Reference31 articles.

1. Prediction of mechanical properties of hot rolled strips based on support vector quantile regression with adaptive QLASSO;he;Funct Mater,2022

2. MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks

3. Prediction of mechanical properties of welded steel X70 pipeline using neural network modelling

4. Mechanical Property Prediction of Strip Model Based on PSO-BP Neural Network

5. Prediction of the mechanical properties of hot-rolled CMn steels using artificial neural networks

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