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Information-entropic analysis of chaotic time series: determination of time-delays and dynamical coupling

机译:混沌时间序列的信息熵分析:时滞和动力耦合的确定

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By calculating the conditional entropy of two different chaotic time series. converted into symbolic sequences through the application of prescribed (but otherwise arbitrary) rules. it can be determined whether or not these originate from the same underlying dynamics. We show that by comparing the conditional entropy of a sequence. obtained by coarse-graining of a chaotic time series, with respect to shifted copies of itself. time-delays that may be inherent in the dynamics can be found. Application is made to time-series obtained front dynamical systems such as Mackey-Glass equation and Ikeda equation. The method appears equally effective in determining the dynamical coupling of climatic time signals. Our results are robust to additive noise. and can thus be applied even when the conversion from a time series to a symbolic sequence has a small proportion of errors. (C) 2002 Elsevier Science Ltd. All rights reserved. [References: 33]
机译:通过计算两个不同混沌时间序列的条件熵。通过使用规定的(但任意的)规则转换为符号序列。可以确定它们是否源自相同的基础动力学。我们通过比较序列的条件熵来表明这一点。通过对自身的移位副本进行混沌时间序列的粗粒度获得。可以发现动力学中固有的时间延迟。适用于按时间顺序获得的前动力学系统,例如Mackey-Glass方程和Ikeda方程。该方法在确定气候时间信号的动态耦合方面似乎同样有效。我们的结果对加性噪声具有鲁棒性。因此,即使从时间序列到符号序列的转换具有很小比例的误差,它也可以应用。 (C)2002 Elsevier ScienceLtd。保留所有权利。 [参考:33]

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