混沌时间序列的Volterra自适应预测滤波器定阶
Determining rank of volterra adaptive filter of chaotic time series
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摘要: 由于Volterra自适应滤波器的阶数对预测性能有较大的影响,在实际预测中,如何确定Volterra自适应滤波器的最优阶数就成为一个关键问题,该文运用相空间重构理论,推导出了Volterra自适应滤波器的最优阶数等于混沌动力系统的最小嵌入维数,作者用六种混沌时间序列进行实验,结果表明这种定阶方法在混沌时间序列Volterra自适应预测中非常成功,该方法对噪声影响的变化,表现出较好的鲁棒性。Abstract: As the rank of Volterra adaptive filter interferes with predictive performance, how to determine the optimal rank of Volterra adaptive filter becomes a key problem in practical prediction. Using theory of phase space reconstruction, this paper derives that the optimal rank of Volterra adaptive filter equals the lowest embedding dimension of chaotic dynamical systems. It is shown through some chaotic series experiments that this method is successful in Volterra adaptive predication and robust to the noise of different levels added to the chaotic time series.
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