Identifying early-warning signals of critical transitions with strong noise by dynamical network markers.
Identifying early-warning signals of critical transitions with strong noise by dynamical network markers.
复制标题
通过动态网络标记识别具有强噪声的关键转变的预警信号
DOI：
10.1038/srep17501
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发表时间：
2015-12-09
影响因子：
4.6
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Liu R;Chen P;Aihara K;Chen L
复杂系统临界跃迁的预警信号识别是一个难点，尤其是当目标系统受到大噪声干扰时，由于观测数据的强烈波动，传统方法难以有效识别。在本文中，我们证明了当噪声不足够小时，临界跃迁不是传统的状态跃迁而是概率分布跃迁，而这在真实的系统中是普遍存在的.我们提出了一个无模型的计算方法来检测这种转变之前的警告信号。背后的关键思想是一个策略：“使大噪声更小”的分布嵌入方案，将数据从观测到的状态变量与大噪声的分布变量与小噪声，从而使传统的标准有效，因为显着减少波动。具体来说，通过将系统从状态动力学改变为概率分布动力学的矩扩展来增加观测数据的维度，我们在高维空间中获得新的数据，但噪声要小得多。然后，我们开发了一个标准的基础上的动态网络标记（DNM）信号即将发生的临界转变，使用转换的高维数据。我们还证明了我们的方法在生物，生态和金融系统的有效性。
Identifying early-warning signals of a critical transition for a complex system is difficult, especially when the target system is constantly perturbed by big noise, which makes the traditional methods fail due to the strong fluctuations of the observed data. In this work, we show that the critical transition is not traditional state-transition but probability distribution-transition when the noise is not sufficiently small, which, however, is a ubiquitous case in real systems. We present a model-free computational method to detect the warning signals before such transitions. The key idea behind is a strategy: “making big noise smaller” by a distribution-embedding scheme, which transforms the data from the observed state-variables with big noise to their distribution-variables with small noise and thus makes the traditional criteria effective because of the significantly reduced fluctuations. Specifically, increasing the dimension of the observed data by moment expansion that changes the system from state-dynamics to probability distribution-dynamics, we derive new data in a higher-dimensional space but with much smaller noise. Then, we develop a criterion based on the dynamical network marker (DNM) to signal the impending critical transition using the transformed higher-dimensional data. We also demonstrate the effectiveness of our method in biological, ecological and financial systems.