Inferring phase equations from multivariate time series
Inferring phase equations from multivariate time series
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DOI：
10.1103/physrevlett.99.064101
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发表时间：
2007-08-10
影响因子：
8.6
通讯作者：
Hudson, John L.
中科院分区：
文献类型：
--
作者：
Tokuda, Isao T.;Jain, Swati;Hudson, John L.
提出了一种从弱耦合极限环振子网络记录的多变量时间序列数据中提取相位方程的方法。我们的目的是估计相位方程的重要性质，包括固有频率和振子之间的相互作用函数。我们的方法需要测量振荡的实验观测值；与以前的方法相比，它不需要在孤立的单振荡器或双振荡器设置中进行测量。这种非侵入性技术在生物系统中是有利的，在生物系统中提取少数振荡器可能是一项困难的任务。当数据来自非同步状态时，该方法是最有效的。利用电化学振荡器网络证明了实验系统的适用性；利用得到的相位模型预测系统的同步图。
An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.