Title :
An adaptive RBF network optimised using a genetic algorithm applied to rainfall forecasting
Author :
Jareanpon, C. ; Pensuwon, W. ; Frank, R.J. ; Davey, N.
Author_Institution :
Dept. of Informatics, Mahasarakham Univ., Thailand
Abstract :
Rainfall prediction is a challenging task, especially in a modern world facing the major environmental problem of global warming. The proposed method uses an adaptive radial basis function neural network mode with a specially designed genetic algorithm (GA) to obtain the optimal model parameters. A significant feature of the adaptive radial basis function network is that it is able create new hidden units and solve the spread factor problem using a genetic algorithm. It is shown that the evolved parameter values improved performance.
Keywords :
computer applications; genetic algorithms; prediction theory; radial basis function networks; rain; weather forecasting; adaptive RBF network; adaptive radial basis function network; genetic algorithm; neural network; rainfall forecasting; rainfall prediction; spread factor problem; time series; Adaptive systems; Artificial neural networks; Function approximation; Genetic algorithms; Informatics; Neural networks; Neurons; Piecewise linear approximation; Radial basis function networks; Rain;
Conference_Titel :
Communications and Information Technology, 2004. ISCIT 2004. IEEE International Symposium on
Print_ISBN :
0-7803-8593-4
DOI :
10.1109/ISCIT.2004.1413871
