用于识别矩阵复杂耦合映射中时空发散嵌合体状态的图像熵

Image Entropy for the Identification of Chimera States of Spatiotemporal Divergence in Complex Coupled Maps of Matrices.

作者信息

Smidtaite Rasa, Lu Guangqing, Ragulskis Minvydas

机构信息

Center for Nonlinear Systems, Kaunas University of Technology, Studentu 50-146, LT-51368 Kaunas, Lithuania.

Department of Applied Mathematics, Kaunas University of Technology, Studentu 50-318, LT-51368 Kaunas, Lithuania.

出版信息

Entropy (Basel). 2019 May 24;21(5):523. doi: 10.3390/e21050523.

DOI:10.3390/e21050523

PMID:33267237

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7515012/

Abstract

Complex networks of coupled maps of matrices (NCMM) are investigated in this paper. It is shown that a NCMM can evolve into two different steady states-the quiet state or the state of divergence. It appears that chimera states of spatiotemporal divergence do exist in the regions around the boundary lines separating these two steady states. It is demonstrated that digital image entropy can be used as an effective measure for the visualization of these regions of chimera states in different networks (regular, feed-forward, random, and small-world NCMM).

摘要

本文研究了矩阵耦合映射的复杂网络（NCMM）。结果表明，一个NCMM可以演化为两种不同的稳态——静止状态或发散状态。在分隔这两种稳态的边界线周围的区域中，似乎确实存在时空发散的嵌合态。结果表明，数字图像熵可以用作一种有效手段，用于可视化不同网络（规则、前馈、随机和小世界NCMM）中的这些嵌合态区域。