Browse > Article
http://dx.doi.org/10.5370/KIEE.2015.64.1.099

Design of RBF Neural Networks Based on Recursive Weighted Least Square Estimation for Processing Massive Meteorological Radar Data and Its Application  

Kang, Jeon-Seong (Dept. of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.64, no.1, 2015 , pp. 99-106 More about this Journal
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.
Keywords
RBFNN; Pattern Classifier; Recursive Weighted Least Square Estimation(RWLSE); Fuzzy C-mean(FCM); Big Radar Data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. B. Roh, S. K. Oh, and W. Pedrycz. "Design of fuzzy radial basis function-based polynomial neural networks." Fuzzy sets and systems Vol 185, pp 15-37. December 2011   DOI   ScienceOn
2 Walther, A., Schröder, M., Fischer, J., & Bennartz, R. (2009). Comparison of precipitation in the regional climate model BALTIMOS to radar observations. Theoretical and Applied Climatology, 1-14.
3 Berenguer, M., Sempere-Torres, D., Corral, C., & Sánchez-Diezma, R. (2006). A fuzzy logic technique for identifying nonprecipitating echoes in radar scans. Journal of Atmospheric and Oceanic Technology, 23(9), 1157-1180.   DOI   ScienceOn
4 S. K. Oh, W. D. Kim, and W. Pedrycz, "Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization." Fuzzy Sets and Systems, Vol 237, pp 96-117, February 2014   DOI   ScienceOn
5 Kuo, Yi‐Ming, Hone‐Jay Chu, and Tsung‐Yi Pan. “Temporal precipitation estimation from nearby radar reflectivity using dynamic factor analysis in the mountainous watershed–a case during Typhoon Morakot.” Hydrological Processes 28.3 2014
6 S-K. Oh, W-D. Kim, and W. Pedrycz, “Polynomial based radial basis function neural networks (RBFNN) realized with the aid of particle swarm optimization,” Fuzzy Sets and Systems, Vol. 163, No. 1, pp. 54-77, 2011   DOI   ScienceOn
7 Wang, Cheng, and Tao Tang. "Recursive least squares estimation algorithm applied to a class of linear-inparameters output error moving average systems." Applied Mathematics Letters 29 (2014): 36-41.
8 W.-D. Kim, S.-K. Oh, K.-S. Seo, and W. Pedrycz, “Growing Rule-based Fuzzy Model Developed with the Aid of Fuzzy Clustering ”, IFSA World Congress & NAFIPS Annual Meeting, pp. 573-578, June 24-28, 2013.
9 Ding, Shifei, and Xiaopeng Hua. "Recursive least squares projection twin support vector machines for nonlinear classification." Neurocomputing 130 (2014): 3-9.   DOI   ScienceOn
10 Rinnan, Åsmund, et al. "Recursive weighted partial least squares (rPLS): an efficient variable selection method using PLS." Journal of Chemometrics 28.5 (2014): 439-447.   DOI   ScienceOn