论文标题

具有惯性和Hebbian学习的库拉莫托模型的低维行为

Low-Dimensional Behavior of a Kuramoto Model with Inertia and Hebbian Learning

论文作者

Ruangkriengsin, Tachin, Porter, Mason A.

论文摘要

我们在具有惯性和Hebbian学习的库拉莫托模型中研究低维动力学。在此模型中,振荡器之间的耦合强度取决于振荡器之间的相位差异和根据HEBBIAN学习规则变化。我们分析了两个耦合振荡器的特殊情况,该振荡器产生了一个五维动力学系统,该系统将其分解为二维纵向系统和三维横向系统。我们很容易地编写纵向系统的精确解决方案,然后我们将注意力集中在横向系统上。我们使用线性稳定性分析对横向系统平衡点的稳定性进行了分类。我们表明横向系统是耗散的,其所有轨迹最终都局限于有限区域。我们计算Lyapunov指数推断横向系统的可能限制行为,并划分了三种定性不同行为的参数区域。利用对低维动力学分析的见解,我们研究了原始的高维系统,在这种情况下,我们从具有不同方差的高斯分布中绘制振荡器的内在频率。

We study low-dimensional dynamics in a Kuramoto model with inertia and Hebbian learning. In this model, the coupling strength between oscillators depends on the phase differences between the oscillators and changes according to a Hebbian learning rule. We analyze the special case of two coupled oscillators, which yields a five-dimensional dynamical system that decouples into a two-dimensional longitudinal system and a three-dimensional transverse system. We readily write an exact solution of the longitudinal system, and we then focus our attention on the transverse system. We classify the stability of the transverse system's equilibrium points using linear stability analysis. We show that the transverse system is dissipative and that all of its trajectories are eventually confined to a bounded region. We compute Lyapunov exponents to infer the transverse system's possible limiting behaviors, and we demarcate the parameter regions of three qualitatively different behaviors. Using insights from our analysis of the low-dimensional dynamics, we study the original high-dimensional system in a situation in which we draw the intrinsic frequencies of the oscillators from Gaussian distributions with different variances.

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