论文标题

使用机器学习识别嵌合体

Identification of Chimera using Machine Learning

论文作者

Ganaie, M. A., Ghosh, Saptarshi, Mendola, Naveen, Tanveer, M, Jalan, Sarika

论文摘要

嵌合体状态是指在各种复杂动力学系统中发现的相同耦合的动力学单元中相干和非固相的共存。一方面,由于其在包括神经科学在内的各个领域的适用性,嵌合体的识别是必不可少的,另一方面由于其在不同系统中的外观广泛,其概况的特殊性质都具有挑战性。因此,一种简单而通用的识别方法仍然是一个空旷的问题。在这里,我们提出了一种使用机器学习技术来表征不同动态阶段的非常独特的方法,并通过使用各种不同模型生成的给定空间剖面从给定的空间轮廓中识别嵌合体状态。实验结果表明,分类算法的性能在不同的动力学模型中有所不同。机器学习算法,即随机森林,基于Tikhonov的倾斜随机森林,平行轴拆分和空空间正则正规化可实现超过$ 96 \%$ $的准确性。对于逻辑图,基于随机的森林和基于Tikhonov正则化的随机森林显示超过$ 90 \%$的准确性,对于Hénon-Map模型,随机森林,空空间和基于轴平行的基于基于偏分的正则化倾斜随机森林,实现了超过$ 80 \%$ $精度。具有无效空间正则化的倾斜随机森林在不同的动态模型上实现了一致的性能(超过$ 83 \%$的精度),而自动编码器基于自动编码器的随机矢量功能链接神经网络的性能相对较低。这项工作为采用机器学习技术提供了一个方向,以识别在大规模上耦合的非线性单元中产生的动态模式,并在现实世界中用于各种应用程序中的复杂时空模式来表征复杂的时空模式。

Chimera state refers to coexistence of coherent and non-coherent phases in identically coupled dynamical units found in various complex dynamical systems. Identification of Chimera, on one hand is essential due to its applicability in various areas including neuroscience, and on other hand is challenging due to its widely varied appearance in different systems and the peculiar nature of its profile. Therefore, a simple yet universal method for its identification remains an open problem. Here, we present a very distinctive approach using machine learning techniques to characterize different dynamical phases and identify the chimera state from given spatial profiles generated using various different models. The experimental results show that the performance of the classification algorithms varies for different dynamical models. The machine learning algorithms, namely random forest, oblique random forest based on tikhonov, parallel-axis split and null space regularization achieved more than $96\% $ accuracy for the Kuramoto model. For the logistic-maps, random forest and tikhonov regularization based oblique random forest showed more than $90\%$ accuracy, and for the Hénon-Map model, random forest, null-space and axis-parallel split regularization based oblique random forest achieved more than $80\%$ accuracy. The oblique random forest with null space regularization achieved consistent performance (more than $83\%$ accuracy) across different dynamical models while the auto-encoder based random vector functional link neural network showed relatively lower performance. This work provides a direction for employing machine learning techniques to identify dynamical patterns arising in coupled non-linear units on large-scale, and for characterizing complex spatio-temporal patterns in real-world systems for various applications.

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