Multi-Steps Prediction For Chaotic Time Series Based On Improved Echo State Network

In this paper,the traditional echo state network(ESN) through the structure and learning mechanism of the study,on the echo state network prediction method of chaotic time series. Because of chaotic time series extreme sensitivity to initial conditions, make the echo state network in chaotic time series prediction of noise are used for non-ideal conditions, but in real life, noise and the sample sequence common coexistence of the situation in which echo state networks in the practical application availability is not strong, difficult to achieve satisfactory predictions.According to the shortages, this paper selects this wavelet analysis combined with echo state networks, propose a modified echo state network to transform loose with the ESN network and the combination of compact type. In chaotic time series prediction to first make ESN network and wavelet transform based on a loose combination of the noisy chaotic time series using wavelet transform denoising ESN to do pretreatment, makes the phase space reconstruction can better determine the phase space of make the chaotic attractor real trajectories of phase space reconstruction, reasonable structure, the calculation error is more reasonable input, Then make ESN network based on wavelet function with tight, replace original type of neuron network, with the new state of echo mechanism of learning network, establish a direct multi-step forecast method, the realization of noise chaotic time series of 5 steps,10 steps that 50 step.Simulation results show that the denoising after samples separately predict with traditional echo state network input use of S-type neurons in the state, improved network compared to the echo state network containing noise experimental data of multi-step forecast, network generalization ability and forecasting precision are large and degree of ascension.