NONLINEAR TIME SERIES PREDICTOR BASED ON GENERALIZED RADIAL BASIS FUNCTION NEURAL NETWORKS
基于广义径向基函数神经网络的非线性时间序列预测器

The architecture and learning algorithm of traditional radial basis function (RBF) neural networks are surveyed in this paper. A generalized radial basis function model is proposed, which is more flexible and extensible. Based on the numerical solution to Mackey-Glass hematopoietic model equation, the prediction results obtained by radial basis function (RBF) model, gradient radial basis function (GRBF) model, and the generalized radial basis function model are compared and discussed, which show the effectiveness of the generalized model.