集体振荡网络的相振幅减小和最佳相锁定
Petar Mircheski1, Jinjie Zhu2, Hiroya Nakao1
1Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan.
Chaos (Woodbury, N.Y.)
|October 13, 2023
概括
我们引入了相振幅减小框架来分析网络系统中的集体振荡. 这种方法捕捉了超出极限周期的动态,增强了对网络响应和同步的理解.
科学领域:
- 动态系统理论 动态系统理论
- 网络科学 网络科学
- 计算神经科学是一种计算神经科学.
背景情况:
- 在生物和人工网络中,集体振荡是基本的.
- 现有的相位减小方法通常通过专注于极限周期来简化动态.
- 了解极限周期动态的偏差对于复杂的网络行为至关重要.
研究的目的:
- 开发一种新的相振幅减小框架,用于分析网络动态系统中的集体振荡.
- 通过结合振幅动态来扩展传统相位减速的功能.
- 为研究网络对外部输入和合的响应提供一个工具,包括同步和相锁.
主要方法:
- 在已建立的相位减小技术基础上开发了相位减小幅度框架.
- 嵌入的振幅变量与相位变量一起,以捕捉极限周期动态的偏差.
- 通过使用数值模拟,将框架应用于FitzHugh-Nagumo元素网络.
主要成果:
- 阶段幅度减小框架有效地分析了集体振荡和偏离不扰动态的偏差.
- 在由FitzHugh-Nagumo元素组成的网络上证明了框架的有效性.
- 导出的相振幅方程为网络同步和稳定提供了洞察力.
结论:
- 阶段幅度减小框架提供了比单相方法更全面的网络动态系统分析.
- 该框架有助于研究复杂网络中的同步,相锁定和稳定性.
- 导出的方程可以为网络行为的最佳控制策略的设计提供信息.
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