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From Random Poincar, Maps to Stochastic Mixed-Mode-Oscillation Patterns

机译:从随机Poincar映射到随机混合模式振荡模式

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We quantify the effect of Gaussian white noise on fast-slow dynamical systems with one fast and two slow variables, which display mixed-mode oscillations owing to the presence of a folded-node singularity. The stochastic system can be described by a continuous-space, discrete-time Markov chain, recording the returns of sample paths to a Poincar, section. We provide estimates on the kernel of this Markov chain, depending on the system parameters and the noise intensity. These results yield predictions on the observed random mixed-mode oscillation patterns. Our analysis shows that there is an intricate interplay between the number of small-amplitude oscillations and the global return mechanism. In combination with a local saturation phenomenon near the folded node, this interplay can modify the number of small-amplitude oscillations after a large-amplitude oscillation. Finally, sufficient conditions are derived which determine when the noise increases the number of small-amplitude oscillations and when it decreases this number.
机译:我们用一个快变量和两个慢变量来量化高斯白噪声对快慢动力系统的影响,这些快变量由于存在折叠节点奇点而显示混合模式振荡。随机系统可以用连续空间,离散时间的马尔可夫链描述,记录样本路径到庞加莱区的返回。我们根据系统参数和噪声强度提供有关此马尔可夫链内核的估计。这些结果产生了对观察到的随机混合模式振荡模式的预测。我们的分析表明,小振幅振荡的数量与整体返回机制之间存在复杂的相互作用。结合折叠节点附近的局部饱和现象,这种相互作用可以在大振幅振荡之后修改小振幅振荡的次数。最后,得出足够的条件,这些条件确定噪声何时增加小幅度振荡的数量,何时降低该数量。

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