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http://dx.doi.org/10.5391/JKIIS.2014.24.5.536

Design of Meteorological Radar Pattern Classifier Using Clustering-based RBFNNs : Comparative Studies and Analysis  

Choi, Woo-Yong (Department of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.24, no.5, 2014 , pp. 536-541 More about this Journal
Abstract
Data through meteorological radar includes ground echo, sea-clutter echo, anomalous propagation echo, clear echo and so on. Each echo is a kind of non-precipitation echoes and the characteristic of individual echoes is analyzed in order to identify with non-precipitation. Meteorological radar data is analyzed through pre-processing procedure because the data is given as big data. In this study, echo pattern classifier is designed to distinguish non-precipitation echoes from precipitation echo in meteorological radar data using RBFNNs and echo judgement module. Output performance is compared and analyzed by using both HCM clustering-based RBFNNs and FCM clustering-based RBFNNs.
Keywords
RBFNNs; Echo Judgement module; Fuzzy C-mean Clustering(FCM); Hard C-mean Clustering(HCM);
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