Topological synchronization of chaotic systems

吸引子 多重分形系统 李雅普诺夫指数 混沌同步 同步(交流) 拓扑(电路) 相位同步 混乱的 统计物理学 分形维数 分形 计算机科学 物理 数学 控制理论(社会学) 人工智能 数学分析 控制(管理) 组合数学
作者
Nir Lahav,I. Sendiña–Nadal,Chittaranjan Hens,Baruch Ksherim,Baruch Barzel,Reuven Cohen,Stefano Boccaletti
出处
期刊:Scientific Reports [Springer Nature]
卷期号:12 (1) 被引量:23
标识
DOI:10.1038/s41598-022-06262-z
摘要

A chaotic dynamics is typically characterized by the emergence of strange attractors with their fractal or multifractal structure. On the other hand, chaotic synchronization is a unique emergent self-organization phenomenon in nature. Classically, synchronization was characterized in terms of macroscopic parameters, such as the spectrum of Lyapunov exponents. Recently, however, we attempted a microscopic description of synchronization, called topological synchronization, and showed that chaotic synchronization is, in fact, a continuous process that starts in low-density areas of the attractor. Here we analyze the relation between the two emergent phenomena by shifting the descriptive level of topological synchronization to account for the multifractal nature of the visited attractors. Namely, we measure the generalized dimension of the system and monitor how it changes while increasing the coupling strength. We show that during the gradual process of topological adjustment in phase space, the multifractal structures of each strange attractor of the two coupled oscillators continuously converge, taking a similar form, until complete topological synchronization ensues. According to our results, chaotic synchronization has a specific trait in various systems, from continuous systems and discrete maps to high dimensional systems: synchronization initiates from the sparse areas of the attractor, and it creates what we termed as the 'zipper effect', a distinctive pattern in the multifractal structure of the system that reveals the microscopic buildup of the synchronization process. Topological synchronization offers, therefore, a more detailed microscopic description of chaotic synchronization and reveals new information about the process even in cases of high mismatch parameters.
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