Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

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

클러스터링 기반 RBFNNs를 이용한 기상레이더 패턴분류기 설계 : 비교 연구 및 해석

  • Choi, Woo-Yong (Department of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
  • Received : 2014.03.09
  • Accepted : 2014.09.23
  • Published : 2014.10.25
Download PDF
⟨ Previous Next ⟩

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.

기상레이더를 통해 취득된 데이터에는 지형에코, 파랑에코, 이상에코, 그리고 청천에코등이 존재한다. 각 에코는 여러 종류의 비강수에코이고, 이 비강수에코를 제거하기 위해 각 에코들의 특성을 분석하였다. 기상레이더 데이터는 매우 방대한 양이기 때문에 전처리 절차를 통해 분석된다. 본 논문에서는 클러스터링 기반 방사형 기저함수 신경회로망(RBFNNs : Radial Basis Function Neural Networks)과 에코 판단 모듈을 이용하여 기상레이더 데이터에서 강수에코와 비강수에코들을 구별하기 위한 에코 패턴분류기를 설계하였다. HCM(Hard C-Mean) 클러스터링 기반 RBFNNs 와 FCM(Fuzzy C-Mean) 클러스터링 기반 RBFNNs를 이용하여 출력성능은 비교 및 분석된다.

Keywords

References

  1. Cho, Y. H., G. Lee, K. E. Kim, and I. Zawadzki, "Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes." J. Atmos. Oceanic Technol., 23, 1206-1222 (2006). https://doi.org/10.1175/JTECH1913.1
  2. Tanvir Islam, Miguel A. Rico-Ramirez, Dawei Han, Prashant K. Srivastava, "Artificial intelligence techniques for clutter identification with polarimetric radar signatures." Atmospheric Research, Volumes 109-110, June, Pages 95-113 (2012). https://doi.org/10.1016/j.atmosres.2012.02.007
  3. Walther, A., Schroder, M., Fischer, J., & Bennartz, R. "Comparison of precipitation in the regional climate model BALTIMOS to radar observations." Theoretical and Applied Climatology, 1-14. (2009).
  4. Berenguer, M., Sempere-Torres, D., Corral, C., & Sanchez-Diezma, R. "A fuzzy logic technique for identifying nonprecipitating echoes in radar scans." Journal of Atmospheric and Oceanic Technology, 23(9), 1157-1180. (2006). https://doi.org/10.1175/JTECH1914.1
  5. G. Pajares, M. Guijarro, A. Ribeiro, "A Hopfield Neural Network for combining classifiers applied to textured images." Neural Networks, Vol.23, pp. 144-153, (2010). https://doi.org/10.1016/j.neunet.2009.07.019
  6. S. Abbasbandy, M. Otadi, M. Mosleh," Numerical solution of a system of fuzzy polynomials by fuzzy neural network." Information Sciences, 178 (8) 1948-1960. (2008) https://doi.org/10.1016/j.ins.2007.11.026
  7. S.-K. Oh, W. Pderycz, B.-J. Park, "Self-organizing neurofuzzy networks in modeling software data," Fuzzy Sets and Systems, vol. 145, pp. 165-181, (2004). https://doi.org/10.1016/j.fss.2003.10.009
  8. L. Sanchez, I. Couso, J.A. Corrales, "Combining GP operators with SA search to evolve fuzzy rule based classifiers." Information Science, 136 175-191. (2001). https://doi.org/10.1016/S0020-0255(01)00146-3
  9. S. K. Oh, W. D. Kim, and W. Pedrycz,‟Polynomial based radial basis function neural networks(P-RBF NNs) realized with the aid of particle swarm optimization," Fuzzy Sets and Systems, Vol. 163, No. 1, pp. 54-77, 2011. https://doi.org/10.1016/j.fss.2010.08.007
  10. W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks," IEEE Trans. Neural Networks, vol. 9, pp. 601-612, July. (1998). https://doi.org/10.1109/72.701174

Cited by

  1. Design of Pattern Classifier for Electrical and Electronic Waste Plastic Devices Using LIBS Spectrometer vol.26, pp.6, 2016, https://doi.org/10.5391/JKIIS.2016.26.6.477