Title :
A new model of synthetic integration for meteorological forecast based on neural networks and fuzzy logic
Author :
Weihong, Wang ; Min, Yao
Author_Institution :
Dept. of Comput. Eng., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
Combination methods of neural networks and fuzzy logic are briefly surveyed. Then, a novel combination model is presented for synthetic integration of rainfall. The presented model is composed of four network layers: input layer, membership function construction layer, inference layer and defuzzification layer. The combination model is applied to synthetic integration of forecasted rainfall data produced by gradual regression method, periodic analysis plus multi-layer method and model output statistics method. The model is trained by short-term rainfall data of Zhejiang Province from 1980 to 1997. The synthetic integration (forecast) results from 1998 to 2000 show that the presented model can obtain satisfactory forecast performance.
Keywords :
fuzzy logic; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); rain; statistical analysis; weather forecasting; Zhejiang Province; defuzzification layer; fuzzy logic; gradual regression method; inference layer; input layer; membership function construction layer; meteorological forecast; model output statistics method; multi-layer method; neural networks; periodic analysis; rainfall; rainfall forecast data; synthetic integration; Demand forecasting; Economic forecasting; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Meteorology; Neural networks; Predictive models; Technology forecasting; Weather forecasting;
Conference_Titel :
Signal Processing, 2002 6th International Conference on
Print_ISBN :
0-7803-7488-6
DOI :
10.1109/ICOSP.2002.1180159
