在复杂的振荡器网络中对同步的对抗性控制
Yasutoshi Nagahama1, Kosuke Miyazato1, Kazuhiro Takemoto1,2
1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
Chaos (Woodbury, N.Y.)
|October 3, 2025
概括
研究人员使用深度学习启发的对抗性攻击来控制振荡器网络中的同步. 小相干扰可以显著增强或抑制集体同步,为网络管理提供一种新的方法.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 动态系统理论 动态系统理论
- 人工智能的人工智能
背景情况:
- 控制复杂网络中的同步对于理解物理学,生物学和工程学的现象至关重要.
- 传统方法通常需要大量的能量投入或全球网络信息.
- 深度学习的对抗性攻击原则为有针对性的系统操纵提供了新的视角.
研究的目的:
- 开发一种新的扰动策略,用于控制库拉莫托振荡器网络中的同步动态.
- 为了利用深度学习概念,特别是对抗性攻击,实现精确的同步管理.
- 研究小相干扰在增强或抑制集体同步的有效性.
主要方法:
- 将同步控制作为基于梯度的优化问题的制定.
- 对单个振荡器相的顺序参数梯度的计算.
- 识别最佳的小相干扰以引导网络同步.
主要成果:
- 证明极小的相位扰动可以在各种网络架构中实现显著的同步控制.
- 同步增强在各种网络大小中是有效的.
- 同步抑制在较大的网络中特别有效,可观察到可扩展性.
结论:
- 拟议的对抗框架为联网动态系统中的同步管理提供了一个新的范式.
- 深度学习启发的扰动策略提供了一种有效的方法来控制集体行为.
- 该方法在各种正规和现实世界的网络拓上得到了验证,包括电网和脑网络.
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