用一种少参数非线性自适应滤波器自适应预测低维混沌时间序列

基于混沌动力系统的相空间延迟坐标重构，利用混沌序列固有的确定性和非线性，提出了用 于混沌时间序列预测的一种少参数非线性自适应滤波预测模型.该预测模型在Volterra自适 应滤波器的基础上引入sigmoid函数来减少待定参数.实验研究表明，这种少参数非线性自适 应滤波预测器仅需用50个样本经20次预训练后，就能有效地预测一些低维混沌序列，且这种 少参数非线性自适应滤波预测器更便于工程实现.关键词：混沌非线性自适应预测少参数非线性自适应滤波器自适应算法

NONLINEAR ADAPTIVE PREDICTION OF CHAOTIC TIME SERIES WITH A REDUCED PARAMETER NO NLINEAR ADAPTIVE FILTER

Based on the deterministic and nonlinear characterization of the chaotic signals, a new reduced parameter nonlinear adaptive filter is proposed to make adaptive predictions of chaotic time series. The sigmoid function is introduced to nonlinear predictive filter for reducing unknown parameters of the second-order Volterra filters. A reduced parameter nonlinear adaptive filtering prediction schemeis suggested in order to track current chaotic trajectory by using precedent predictive error for adjusting filter parameters rather than approximating global o r local map of chaotic series. Experimental results show that this reduced param eter nonlinear adaptive filter, which is only trained with 50 samples and 20 ite rations, can be successfully used to make one-step and multi-step predictions of chaotic time series.