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arXiv 2203.01663nlin.AO
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从时间序列数据估计极限环振子的渐近相位和振幅函数

Estimating asymptotic phase and amplitude functions of limit-cycle oscillators from time series data

Norihisa Namura, Shohei Takata, Katsunori Yamaguchi, Ryota Kobayashi, Hiroya Nakao

摘要:

我们提出了一种方法来估计渐近相位和振幅函数的极限环振子使用观测的时间序列数据,而无需事先知道他们的动力学方程。估计是由多项式回归,可以解决作为一个凸优化问题。以二维极限环振子为例,数值验证了该方法的有效性。作为一个应用程序,我们证明了数据驱动的快速夹带与振幅抑制使用的最佳周期性输入来自估计的相位和振幅函数。

英文摘要:

We propose a method for estimating the asymptotic phase and amplitude functions of limit-cycle oscillators using observed time series data without prior knowledge of their dynamical equations. The estimation is performed by polynomial regression and can be solved as a convex optimization problem. The validity of the proposed method is numerically illustrated by using two-dimensional limit-cycle oscillators as examples. As an application, we demonstrate data-driven fast entrainment with amplitude suppression using the optimal periodic input derived from the estimated phase and amplitude functions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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