Browse > Article
http://dx.doi.org/10.5370/KIEE.2015.64.6.922

Design of Optimized Type-2 Fuzzy RBFNN Echo Pattern Classifier Using Meterological Radar Data  

Song, Chan-Seok (Dept. of Electrical Engineering, The University of Suwon)
Lee, Seung-Chul (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.6, 2015 , pp. 922-934 More about this Journal
Abstract
In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.
Keywords
Radial basis function neural network; Precipitation echo(PRE); Non-precipitation echo(N-PRE); Event classifier; Echo classifier; Radar data; Interval Type-2 based FCM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Rhee. F, Choi B., “Interval type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks” Int. Conf. Fuzzy Syst., pp. 2047-2052, 2007.
2 W. Pedrycz, “Conditional fuzzy clustering in the design of radial basis function neural networks,” IEEE Trans. Neural Networks, vol. 9, pp. 601-612, 1998.   DOI   ScienceOn
3 Andi Walther, Marc Schröder, Jürgen Fischer, and Ralf Bennartz, “Comparison of precipitation in the regional climate model BALTIMOS to radar observations.” Theoretical and Applied Climatology, vol 118, pp.627-640, 2009.
4 Yo-Han Cho, Gyu Won Lee, Kyung-Eak Kim, and Isztar Zawadzki, “Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes.” J. Atmos. Oceanic Technol, vol. 23, pp.1206–1222, 2006.   DOI   ScienceOn
5 Tanvir Islam, Miguel A. Rico-Ramirez, Dawei Han, and Prashant K. Srivastava, “Artificial intelligence techniques for clutter identification with polarimetric radar signatures.” Atmospheric Research, vol. 109–110, pp. 95–113, 2012.
6 Marc Berenguer, Daniel Sempere-Torres, Carles Corral, and Rafael Sánchez-Diezma, “A fuzzy logic technique for identifying nonprecipitating echoes in radar scans.” J. Atmos. Oceanic Technol,, vol.23, pp.1157-1180, 2006.   DOI   ScienceOn
7 Kilambi. A, A. Bellon, A. Singh, I. Zawadzki, C. Page and F. Fabry, “RAPID: A radar data analysis, processing and interactive display system”, 28th Int Conf. on Radar Meteorology, pp.220-221. 1997.
8 G. Pajares, M. Guijarro, and A. Ribeiro, “A Hopfield Neural Network for combining classifiers applied to textured images.” Neural Networks, vol.23, pp.144-153, 2010.   DOI   ScienceOn
9 S. Abbasbandy, M. Otadi, and M. Mosleh, “Numerical solution of a system of fuzzy polynomials by fuzzy neural network.” Information Sciences vol.178, pp.1948-1960, 2008.   DOI   ScienceOn
10 J. H. Ko, S. K. Oh, and Y. H, Lee, “Design of Echo Pattern Classifier Using Fuzzy Neural Networks”, Proceedings of KIIS Fall Conference, Vol. 23-2, pp.19-20, 2013.