Prediction of Multivariate Chaotic Time Series Via Radial Basis Function Neural Network

In this article, a new multivariate radial basis functions neural network model is proposed to predict the complex chaotic time series. To realize the reconstruction of phase space, we apply the mutual information method and false nearest-neighbor method to obtain the crucial parameters time delay and embedding dimension, respectively, and then expand into the multivariate situation. We also proposed two the objective evaluations, mean absolute error and prediction mean square error, to evaluate the prediction accuracy. To illustrate the prediction model, we use two coupled Rossler systems as examples to do simultaneously single-step prediction and multistep prediction, and find that the evaluation performances and prediction accuracy can achieve an excellent magnitude. (C) 2013 Wiley Periodicals, Inc. Complexity 18: 55-66, 2013