A Comparison of the Machine Learning Algorithm for Evaporation Duct Estimation
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Date
2013-06
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ORCID
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Referee
Mark
Journal Title
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Publisher
Společnost pro radioelektronické inženýrství
Abstract
In this research, a comparison of the relevance vector machine (RVM), least square support vector machine (LSSVM) and the radial basis function neural network (RBFNN) for evaporation duct estimation are presented. The parabolic equation model is adopted as the forward propagation model, and which is used to establish the training database between the radar sea clutter power and the evaporation duct height. The comparison of the RVM, LSSVM and RBFNN for evaporation duct estimation are investigated via the experimental and the simulation studies, and the statistical analysis method is employed to analyze the performance of the three machine learning algorithms in the simulation study. The analysis demonstrate that the M profile of RBFNN estimation has a relatively good match to the measured profile for the experimental study; for the simulation study, the LSSVM is the most precise one among the three machine learning algorithms, besides, the performance of RVM is basically identical to the RBFNN.
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Citation
Radioengineering. 2013, vol. 22, č. 2, s. 657-661. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2013/13_02_0657_0661.pdf
http://www.radioeng.cz/fulltexts/2013/13_02_0657_0661.pdf
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Peer-reviewed
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en