| Title |
Design of Very Short-term Precipitation Forecasting Classifier Based on Polynomial Radial Basis Function Neural Networks for the Effective Extraction of Predictive Factors |
| Authors |
김현명(Kim, Hyun-Myung) ; 오성권(Oh, Sung-Kwun) ; 김현기(Kim, Hyun-Ki) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.1.128 |
| Keywords |
RBFNNs(Radial Basis Function Neural Networks) ; FCM(Fuzzy C-means Method) ; PSO(Particle Swarm Optimization) ; DE(Differential Evolution) ; precipitation forecasting model and classifier |
| Abstract |
In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data. |