Long-term time-series prediction using radial basis function neural networks

This work presents the application of radial basis function (RBF) network models on the challenging problem of providing accurate long-term prediction for time-series. The non-symmetric variation of the fuzzy means (NSFM) algorithm is used to determine the number and locations of the hidden node RBF centers, whereas the synaptic weights are calculated using linear regression. The proposed approach is applied to the well-known Van der Pol oscillator with the aim of training RBF models so that they can accurately predict the full trajectory of the system, given only its initial state. A comparison with a Runge-Kutta RBF network, highlights the superiority of the proposed method.