The Application of modified ESN in chaotic time series prediction

The parameters selection of ESN (Echo State Network) is excessively dependent on human experience, it is difficult to produce the corresponding optimal parameters for specific problem, resulting in severely restricted in practice. In view of this, a chaotic time series prediction model is proposed in this paper, and the model is based on differential evolution algorithm and the echo state network. With this model, training the input sample sequence to find the network´s parameters which is suitable for the data characteristics at first, then use the ideal parameters to predict chaotic time series. In the prediction of the typical chaotic time series generated by Lorenz system, this method can establish a suitable echo state network based on the data characteristics effectively, and gets satisfactory results.