Time series forecasting with RBF neural network

Radial Basis Function Neural Network (RBF NN) has been widely used for nonlinear system identification because of its simple topological structure and its ability to reveal how learning proceeds in an explicit manner. In this paper, descriptions and original applications of RBF NN, to the time series forecasting problem is presented. Genetic algorithm technique is proposed to improve the RBF center placement quality. The research contributes to the applications of RBF NN by experiments with real-world data sets. Experimental results reveal that the prediction performance of RBF NN is significantly better than a traditional BP NN model.