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。
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.
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。
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.
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。
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.
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