混沌时间序列的双线性自适应预测
Prediction of Chaotic Time Series Using Bilinear Adaptive Filter
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摘要: 基于混沌动力系统相空间的延迟坐标重构和双线性表达式,设计了预测混沌时间序列的双线性自适应 预测滤波器.对2种低维混沌序列的预测实验表明,采用双线性自适应滤波器的预测收敛速度快,处理约50个 样本时即已收敛,预测相对误差小于0.001.Abstract: Based on the delay-coordinate reconstruction and bilinear expressions in the phase space of a chaotic system, a bilinear adaptive filter was designed to predict low-dimensional chaotic time series. Experiments conducted on two examples of low-dimensional chaotic series show that, using the bilinear adaptive filter, the prediction process converges after about 50 samples are processed and the relative prediction error is less than 0.001.
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Key words:
- chaos /
- time series /
- bilinear /
- prediction /
- filters
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