Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

Zhang, D; Zhang, N; Ye, N; Fang, J; Han, X

Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

Zhang, D Zhang, N Ye, N Fang, J

Han, X

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Reliability, 2021, 70, (3), pp. 1-14
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Zhang, D
dc.contributor.author	Zhang, N
dc.contributor.author	Ye, N
dc.contributor.author	Fang, J https://orcid.org/0000-0003-0119-6108
dc.contributor.author	Han, X
dc.date.accessioned	2022-04-26T00:39:24Z
dc.date.available	2022-04-26T00:39:24Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Reliability, 2021, 70, (3), pp. 1-14
dc.identifier.issn	0018-9529
dc.identifier.issn	1558-1721
dc.identifier.uri	http://hdl.handle.net/10453/156592
dc.description.abstract	With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Reliability
dc.relation.isbasedon	10.1109/tr.2020.3001232
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0803 Computer Software, 0906 Electrical and Electronic Engineering
dc.subject.classification	Operations Research
dc.title	Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis
dc.type	Journal Article
utslib.citation.volume	70
utslib.for	0803 Computer Software
utslib.for	0906 Electrical and Electronic Engineering
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 Civil and Environmental Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-07-22T00:00:00+1000Z
dc.date.updated	2022-04-26T00:39:20Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	3

Abstract:

With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method.

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http://hdl.handle.net/10453/156592