混沌时间序列局域零阶预测法性能比较

利用计算机仿真比较了均值、距离加权和指数加权3种混沌局域零阶预测方法的预测精度、抗噪声及多步预测性能．在无噪声或噪声干扰较小时，距离加权预测法的性能最好；当噪声干扰较大时，指数加权预测法预测性能最优．指数加权与均值预测法几乎具有相同的多步预测能力，距离加权预测法的短期预测性能最佳．对于标准的离散混沌时间序列，3种预测方法多步预测误差达到一定值后，不再随预测步长的增加而增加；对于由连续系统抽样得到的混沌时间序列，多步预测误差呈现一定周期性变化．

Performance Comparison of Local Zero-Order Predictive Methods for Chaotic Time Series

Performances of local zero-order prediction methods for chaotic time series were compared in aspects of prediction accuracy, anti-noise and the ability of multi-step prediction through computer simulation. Simulation results show that distance-weighted predictive method is the best when there are no noises or only weak noises; exponential weighted predictive method is better than the others when there are large noises; exponential weighted predictive method and averaging method are basically the same in respect to multi-step prediction, but distance-weighted predictive method is the best in short-term prediction; multi-step prediction errors of standard discrete chaotic time series for the three methods do not increase with prediction step after they arrive at certain values, but for the chaotic time series sampled by continuous system, its multi-step prediction errors have periodicity.