Approximate Reliability Function Based On Wavelet Latin Hypercube Sampling And Bee Recurrent Neural Network

Yeh, W; Su, JC; Hsieh, T; Chih, M; Liu, S

Approximate Reliability Function Based On Wavelet Latin Hypercube Sampling And Bee Recurrent Neural Network

Yeh, W Su, JC Hsieh, T Chih, M Liu, S

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Publisher:: IEEE-Inst Electrical Electronics Engineers Inc
Publication Type:: Journal Article
Citation:: IEEE Transactions on Reliability, 2011, 60 (2), pp. 404 - 414
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Yeh, W	en_US
dc.contributor.author	Su, JC	en_US
dc.contributor.author	Hsieh, T	en_US
dc.contributor.author	Chih, M	en_US
dc.contributor.author	Liu, S	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	IEEE Transactions on Reliability, 2011, 60 (2), pp. 404 - 414	en_US
dc.identifier.issn	0018-9529	en_US
dc.identifier.uri	http://hdl.handle.net/10453/21963
dc.description.abstract	This work combines a Bee Recurrent Neural Network (BRNN) optimized by the Artificial Bee Colony (ABC) algorithm with Monte Carlo Simulation (MCS) to generate a novel approximate model for predicting network reliability. We utilize the Wavelet Transform (	en_US
dc.publisher	IEEE-Inst Electrical Electronics Engineers Inc	en_US
dc.relation.ispartof	IEEE Transactions on Reliability	en_US
dc.relation.isbasedon	10.1109/TR.2011.2134190	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Approximate Reliability Function Based On Wavelet Latin Hypercube Sampling And Bee Recurrent Neural Network	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	60	en_US
utslib.location.activity	ISI:000291317100006	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	ISI:000291317100006	en_US
pubs.embargo.period	Not known	en_US
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 Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	2	en_US
pubs.volume	60	en_US

Abstract:

This work combines a Bee Recurrent Neural Network (BRNN) optimized by the Artificial Bee Colony (ABC) algorithm with Monte Carlo Simulation (MCS) to generate a novel approximate model for predicting network reliability. We utilize the Wavelet Transform (

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http://hdl.handle.net/10453/21963