방대한 기상 레이더 데이터의 원할한 처리를 위한 순환 가중최소자승법 기반 RBF 뉴럴 네트워크 설계 및 응용

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least SquareEstimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, FuzzyC-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connectionweights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function areestimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSEis carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML)dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. Themeteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier isused to efficiently classify these echoes.