Prediction of noisy chaotic time series using an optimal radial basis function neural network.

This paper considers the problem of optimum prediction of noisy chaotic time series using a basis function neural network, in particular, the radial basis function (RBF) network. In the noiseless environment, predicting a chaotic time series is equivalent to approximating a nonlinear function. The optimal generalization is achieved when the number of hidden units of a RBF predictor approaches infinity. When noise exists, it is shown that an optimal RBF predictor should use a finite number of hidden units. To determine the structure of an optimal RBF predictor, we propose a new technique called the cross-validated subspace method to estimate the optimum number of hidden units. While the subspace technique is used to identify a suitable number of hidden units by detecting the dimension of the subspace spanned by the signal eigenvectors, the cross validation method is applied to prevent the problem of overfitting. The effectiveness of this new method is evaluated using simulated noisy chaotic time series as well as real-life oceanic radar signals. Results show that the proposed method can find the correct number of hidden units of an RBF network for an optimal prediction.