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如何检测部分可预测的混沌现象。

How to test for partially predictable chaos.

作者信息

Wernecke Hendrik, Sándor Bulcsú, Gros Claudius

机构信息

Institute for Theoretical Physics, Goethe University Frankfurt, Frankfurt am Main, Germany.

出版信息

Sci Rep. 2017 Apr 24;7(1):1087. doi: 10.1038/s41598-017-01083-x.

Abstract

For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability. Both processes can be either of the same or of very different time scales. In the latter case the two trajectories linger within a finite but small distance (with respect to the overall extent of the attractor) for exceedingly long times and remain partially predictable. Standard tests for chaos widely use inter-orbital correlations as an indicator. However, testing partially predictable chaos yields mostly ambiguous results, as this type of chaos is characterized by attractors of fractally broadened braids. For a resolution we introduce a novel 0-1 indicator for chaos based on the cross-distance scaling of pairs of initially close trajectories. This test robustly discriminates chaos, including partially predictable chaos, from laminar flow. Additionally using the finite time cross-correlation of pairs of initially close trajectories, we are able to identify laminar flow as well as strong and partially predictable chaos in a 0-1 manner solely from the properties of pairs of trajectories.

摘要

对于一个混沌系统,在吸引集上,最初靠近的轨迹对最终会完全不相关。这种去相关过程可以分为初始的指数衰减和随后在混沌吸引子上的扩散过程,从而导致最终可预测性的丧失。这两个过程的时间尺度可以相同,也可以非常不同。在后一种情况下,两条轨迹会在有限但很小的距离内(相对于吸引子的整体范围)徘徊极长的时间,并且仍然部分可预测。标准的混沌测试广泛使用轨道间相关性作为指标。然而,测试部分可预测的混沌大多会产生模糊的结果,因为这种类型的混沌的特征是分形扩展辫子状的吸引子。为了解决这个问题,我们基于最初靠近的轨迹对的交叉距离缩放引入了一种新颖的混沌0-1指标。该测试能够可靠地区分混沌(包括部分可预测的混沌)和层流。此外,利用最初靠近的轨迹对的有限时间互相关性,我们能够仅从轨迹对的性质以0-1的方式识别层流以及强混沌和部分可预测的混沌。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a48/5430683/1140a3bbad8a/41598_2017_1083_Fig1_HTML.jpg

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