- author = "Jill Kobashigawa and Hyoung-sun Youn and Magdy Iskander and Zhengqing Yun",
- title = "Comparison between genetic programming and Neural Network in classification of buried unexploded ordnance (UXO) targets",
- booktitle = "2010 IEEE Antennas and Propagation Society International Symposium (APSURSI)",
- year = "2010",
- month = "11-17 " # jul,
- 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.",
- 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",
-
DOI = "
doi:10.1109/APS.2010.5561278",
- ISSN = "1522-3965",
-
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}",
