THERMAL SCIENCE

International Scientific Journal

Authors of this Paper

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Haibin Li

,

Lijun Sun

,

Qinghu Yao

CORRELATION ANALYSIS BASED ON NEURAL NETWORK COPULA FUNCTION

ABSTRACT
The joint-distribution function between variables plays an important role in reliability analysis. A method is proposed for constructing the function using a neural network, which is used to construct a copula model under arbitrarily measured data, including the input and output values of the neural network using an empirical cumulative distribution. Three traditional copula function models are constructed based on the Kendall rank-correlation coefficients. Based on the Euclidean distance method, the neural network copula and three copula function models are compared.
KEYWORDS
applied mechanics, correlation, neural network copula
PAPER SUBMITTED: 2021-12-15
PAPER REVISED: 2022-06-10
PAPER ACCEPTED: 2022-06-13
PUBLISHED ONLINE: 2023-06-11
DOI REFERENCE: https://doi.org/10.2298/TSCI2303081L
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 3, PAGES [2081 - 2089]
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Correlation analysis based on neural network copula function

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence