Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks

Abstract

We have studied the methodology of estimating the embedding dimension for phase space reconstruction of chaotic time series according to the Takens theorem. We present an approach to the estimation of the embedding dimension based on the prediction of nonlinear performance. That is, we determine the embedding dimension by considering the variation of the performance of prediction model of chaotic time series with embedding dimension. Numerical simulations verify that the method is applicable for determining an appropriate embedding dimension.