Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation Cases

강수/비강수 사례 분류를 위한 RBFNN 기반 패턴분류기 설계

  • Choi, Woo-Yong (Department of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon) ;
  • Kim, Hyun-Ki (Department of Electrical Engineering, The University of Suwon)
  • Received : 2014.03.09
  • Accepted : 2014.09.23
  • Published : 2014.12.25
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Abstract

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

본 연구에서는 인공 벌 군집(ABC: Artificial Bee Colony) 알고리즘을 이용하여 주어진 레이더 데이터로부터 강수 사례와 비강수 사례를 분류하는 방사형 기저함수 신경회로망(RBFNNs: Radial Basis Function Neural Networks)분류기를 소개한다. 기상청에서 사용하고 있는 기상 레이더 데이터의 특성 분석을 통해 입력 데이터를 구성한다. 방사형 기저함수 신경회로망의 조건부에서는 Fuzzy C-Means 클러스터링 방법을 이용하여 적합도를 계산하고, 결론부에서는 최소자승법(LSE: Least Square Method)을 이용하여 다항식 계수를 추정한다. 추론부에서 최종출력 값은 퍼지 추론 방법을 이용하여 얻어진다. 제안된 분류기의 성능은 기상청에서 사용하는 QC와 CZ 데이터를 고려하여 비교 및 분석되어진다.

Keywords

References

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