Comparison between genetic programming and Neural Network in classification of buried unexploded ordnance (UXO) targets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Kobashigawa:2010:APSURSI,
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author = "Jill Kobashigawa and Hyoung-sun Youn and
Magdy Iskander and Zhengqing Yun",
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title = "Comparison between genetic programming and Neural
Network in classification of buried unexploded ordnance
(UXO) targets",
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booktitle = "2010 IEEE Antennas and Propagation Society
International Symposium (APSURSI)",
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year = "2010",
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month = "11-17 " # jul,
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abstract = "In this paper, we present the results of our next step
effort in comparison of classification performances
between the NN and the GP techniques based on the
simulated scattering patterns of UXO-like object and
non-UXO objects. For this comparative study, 2
dimensional scattering images from one UXO target and
four non-UXO objects were generated by numerical
simulation tool (FEKO). For non-UXO objects, the most
challenging targets to discriminate from UXO, since all
these objects produce resonance signal as UXO-like
targets do [6], were selected. Classification
performances of both techniques (NN vs. GP) in
different level of noise and in the case of presence of
untrained data were examined and the results and
observations are discussed.",
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keywords = "genetic algorithms, genetic programming, buried
unexploded ordnance targets, dimensional scattering
images, ground penetrating radar, neural network,
numerical simulation, electrical engineering computing,
ground penetrating radar, neural nets, numerical
analysis",
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DOI = "doi:10.1109/APS.2010.5561278",
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ISSN = "1522-3965",
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notes = "'we confirmed that GP provided better performance than
neural networks'.
Hawaii Center for Advanced Communications College of
Engineering, University of Hawaii at Manoa, USA 96822
Also known as \cite{5561278}",
- }
Genetic Programming entries for
Jill S K Nakatsu
Hyoung-sun Youn
Magdy F Iskander
Zhengqing Yun
Citations