| Title |
Design of RBF Neural Networks Based on Recursive Weighted Least Square Estimation for Processing Massive Meteorological Radar Data and Its Application |
| Authors |
강전성(Kang, Jeon-Seong) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.1.099 |
| Keywords |
RBFNN ; Pattern Classifier ; Recursive Weighted Least Square Estimation(RWLSE) ; Fuzzy C-mean(FCM) ; Big Radar Data |
| Abstract |
In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is 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. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes. |