在神经网络和电力系统中出现奇梅拉式状态
1School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.
Chaos (Woodbury, N.Y.)
|March 25, 2024
概括
像喜梅拉状态这样的同步模式在复杂的网络中出现,包括电网和大脑连接体,即使它们的结构比库拉莫托模型最初预测的更复杂. 这项研究通过数值模拟证实了这些发现.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 计算神经科学是一种神经科学.
- 电力系统工程 电力系统工程
背景情况:
- 库拉莫托类型的模型预测了低光谱维度 (ds<4) 的网络中的部分同步和虚幻状态.
- 现实世界的复杂网络,如电网和大脑连接体,往往表现出比这些模型通常假设的更高的图形尺寸.
研究的目的:
- 用库拉莫托模型研究现实世界复杂网络中发生的部分,挫败的同步和喜梅拉式状态.
- 为欧洲电网中的这些现象提供数值证据,人类连接体 (KKI113) 和果大脑连接体 (FF).
主要方法:
- 一级和二级 (Shinomoto) 库拉莫托模型的数值解决方案.
- 模型应用于欧洲电网和果大脑 (FF) 网络图.
- 对同步结果的分析和在社区和本地秩序参数级别出现的模拟图案的分析.
主要成果:
- 在欧洲电网 (ds<2),KKI113 (dgKKI1133.4(1)) 和FF连接组 (ds<4) 网络中观察到部分,挫败的同步和模拟状态.
- 这些现象发生在高图形尺寸 (dgEU2.6(1),高图形尺寸 (dgFF5.4(1)) 与光谱尺寸的理论值相比.
- 在图形社区层面和通过局部顺序参数确定了类似奇梅拉的模式.
结论:
- 这些发现证实,库拉莫托类型的模型可以表现出复杂的同步行为,包括嵌合体状态,在图形维度高于预期的网络中.
- 这项研究强调了Kuramoto模型对各种复杂系统的适用性,例如电网和生物网络,以了解新出现的同步现象.
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