混沌时间序列基于邻域点的非线性多步自适应预测

根据流形理论，利用混沌时间序列中某点邻域内最近几点的P次迭代像，提出了一种多步自 适应预测算法.仿真说明，这种算法使得预测速度成倍提高，而预测稳定后得到的误差均方 根序列呈指数增长趋势，这个指数就是该混沌时间序列的Lyapunov指数. 关键词： 混沌时间序列 邻域 非线性自适应预测 Lyapunov指数

Nonlinear adaptive multi-step-prediction of chaotic time series based on points in the neighborhood

In this paper a class of nonlinear adaptive multi-step-prediction algorithm base d on the manifold theory was proposed. We have performed the multi-step-predicti on by exploiting images of P-step iterations of several nearest neighbors with t his method. The simulation indicated that this method was available and could im prove the prediction speed, and that the series of the standard deviation of err or after prediction has an exponential growth ratio that is the largest Lyapunov exponent.