CORRELATION ANALYSIS BASED ON NEURAL NETWORK COPULA FUNCTION

LI, H; Sun, L; Yao, Q

CORRELATION ANALYSIS BASED ON NEURAL NETWORK COPULA FUNCTION

LI, H Sun, L

Yao, Q

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Publisher:: VINCA INST NUCLEAR SCI
Publication Type:: Journal Article
Citation:: THERMAL SCIENCE, 2023, 27, (3A), pp. 2081-2089
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	LI, H
dc.contributor.author	Sun, L https://orcid.org/0000-0002-9830-1527
dc.contributor.author	Yao, Q
dc.date.accessioned	2024-03-28T04:18:23Z
dc.date.available	2024-03-28T04:18:23Z
dc.date.issued	2023
dc.identifier.citation	THERMAL SCIENCE, 2023, 27, (3A), pp. 2081-2089
dc.identifier.issn	0354-9836
dc.identifier.issn	2334-7163
dc.identifier.uri	http://hdl.handle.net/10453/177341
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	English
dc.publisher	VINCA INST NUCLEAR SCI
dc.relation.ispartof	THERMAL SCIENCE
dc.relation.isbasedon	10.2298/TSCI2303081L
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0904 Chemical Engineering, 0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy
dc.subject.classification	Energy
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.title	CORRELATION ANALYSIS BASED ON NEURAL NETWORK COPULA FUNCTION
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0904 Chemical Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-28T04:18:22Z
pubs.issue	3A
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	3A

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.

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http://hdl.handle.net/10453/177341