A method based on the third-order Volterra filter for adaptive predictions of chaotic time series
一种基于三阶Volterra滤波器的混沌时间序列自适应预测方法

Based on the Takens' delay coordinate phase reconstruct, we study the third order Volterra filter which is used to make adaptive predictions of chaotic signals. It is approximately implemented by a product coupling configuration; and this filter is used to predict typical low dimensional chaotic time series and high dimensional chaotic electro encephalography(EEG) signal. Simulation results show that: this filter has aprecision 10 3 times higher than the second order Volterra filter when it is used to make predictions of low dimensional chaotic time series. It can be successfully used to make predictions of some high dimensional chaotic EEG signal.