An improved population-based incremental learning method for objects buried in planar layered media

Chen, X; Lei, G; Yang, G; Shao, KR; Guo, Y; Zhu, J; Lavers, JD

An improved population-based incremental learning method for objects buried in planar layered media

Chen, X Lei, G

Yang, G Shao, KR Guo, Y

Zhu, J

Lavers, JD

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Magnetics, 2012, 48 (2), pp. 1027 - 1030
Issue Date:: 2012-02-01

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Field	Value	Language
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802	en_US
dc.contributor.author	Yang, G	en_US
dc.contributor.author	Shao, KR	en_US
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Lavers, JD	en_US
dc.date.issued	2012-02-01	en_US
dc.identifier.citation	IEEE Transactions on Magnetics, 2012, 48 (2), pp. 1027 - 1030	en_US
dc.identifier.issn	0018-9464	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22358
dc.description.abstract	An evolutionary algorithm, the estimation of distribution algorithm (EDA), is used to reconstruct the objects that buried in planar layered media. It is essential that fast forward solvers be used to solve the forward scattering problem for the nonlinear inverse scattering methods, since it can avoid errors by approximation. The EDA is a predominant all-round optimizing method in the macroscopic simulation of evolution process species of nature. Recent studies have shown that the EDA provides better solution for nonlinear problems than the microscopic evolutionary algorithm, such as genetic algorithm (GA) in some cases. The EDA is simpler, both computationally and theoretically, than the GA. We discuss how this can be used to calculate the permittivity and conductivity of the targets. We show preliminary results indicating the potential of reconstruction for buried objects. Compared with other methods, the experiment result shows that the EDA algorithm reduces the number of iteration. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Magnetics	en_US
dc.relation.isbasedon	10.1109/TMAG.2011.2173749	en_US
dc.subject.classification	Applied Physics	en_US
dc.title	An improved population-based incremental learning method for objects buried in planar layered media	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	48	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

An evolutionary algorithm, the estimation of distribution algorithm (EDA), is used to reconstruct the objects that buried in planar layered media. It is essential that fast forward solvers be used to solve the forward scattering problem for the nonlinear inverse scattering methods, since it can avoid errors by approximation. The EDA is a predominant all-round optimizing method in the macroscopic simulation of evolution process species of nature. Recent studies have shown that the EDA provides better solution for nonlinear problems than the microscopic evolutionary algorithm, such as genetic algorithm (GA) in some cases. The EDA is simpler, both computationally and theoretically, than the GA. We discuss how this can be used to calculate the permittivity and conductivity of the targets. We show preliminary results indicating the potential of reconstruction for buried objects. Compared with other methods, the experiment result shows that the EDA algorithm reduces the number of iteration. © 2012 IEEE.

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http://hdl.handle.net/10453/22358