Chaotic Time Series Forecasting Based on Neural Networks
Yongsheng Wang · Journal of Naval Aeronautical and Astronautical University · 2008
The forecast of a special nonlinear system-chaotic system is studied in this paper. The chaotic is a kind of wide spread not line dynamics behavior. Facing the problem of difficulty to estimate and control chaotic time series, the theory of state phase space reconstruction was introduced to analyze the chaotic time series, then the multilayer forward neural network was used to forecast these chaotic time series. The results of predicting the typical Lorenz and Mackey-Glass chaotic time series illustrate that, if there is enough train samples and the neural network has compact structure, the trained network would acquire excellent extensive performance. The effect of the network initial weights was also considered, and an improved way was pointed out in the end.