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混沌时间序列的Volterra自适应预测 被引量:149

PREDICTING LOW-DIMENSIONAL CHAOTIC TIME SERIES USING VOLTERRA ADAPTIVE FILERS 
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摘要 基于混沌动力系统相空间的延迟坐标重构,提出了一种预测混沌时间序列的Volterra自适应滤波预测法,对8种低维混沌序列采用二阶Volterra自适应滤波器进行预测的实验结果表明:当滤波器的长度Nl足够大时,Volterra自适应滤波器能够有效地预测低维混沌时间序列,且Nl的选择不仅与D2有关。 Volterra adaptive filter is used to predict low\|dimensional chaotic time series based on the state space reconstruction of delay\|coordinate embedding of dynamic system.It is shown,through experiments of predicting eight kinds of low\|dimensional chaotic series using second\|order Volterra adaptive filters,that Volterra adaptive filters can accurately predict these chaotic series when the length N l of the Volterra filter is long enough,and the choice of N l is related to D 2 and smoothness of chaotic map.
出处 《物理学报》 SCIE EI CAS CSCD 北大核心 2000年第3期403-408,共6页 Acta Physica Sinica
基金 国防预研基金!(批准号 :98JS0 5 4 1 DZ0 2 0 5 )资助的课题
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