维持库拉莫托模型的混乱动态,通过可适应的水库计算机
Haibo Luo1, Mengru Wang1, Yao Du1
1Shaanxi Normal University, School of Physics and Information Technology, Xi'an 710062, China.
Physical review. E
|December 23, 2025
概括
研究人员使用可适应储存电脑 (RCs) 来创建复杂系统中失败的振荡器的数字双胞胎. 这种方法成功地保持了系统动态,证明了在混乱系统中对组件故障的新方法.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 机器学习 机器学习
背景情况:
- 复杂的系统经常经历组件故障,影响集体动态.
- 在故障期间保持系统功能对于可靠运行至关重要.
研究的目的:
- 提出一种方法来替代空间扩展的混乱系统中故障的组件.
- 为了证明数字双胞胎的有效性,使用可适应的储库计算机创建,以保持系统动态.
主要方法:
- 利用库拉莫托模型来模拟混乱的动态和振荡器故障.
- 采用可适应的储水器计算机 (RCs) 作为基于机器学习的数字双胞胎.
- 训练有素的RC使用系统演变数据来模拟失败的振荡器行为.
主要成果:
- 一个单一的小型RC可以替代单个振荡器,保持系统同步顺序参数.
- 成功替换的持续时间取决于训练数据的大小,合强度和故障组件的数量.
- 即使同步顺序参数在长期不同时,功能网络完整性也得到了维护.
结论:
- 可适应的储计算机提供了一种可行的方法来创建数字双胞胎,以管理混乱系统中的组件故障.
- 这种方法确保在关键的时间内精确地保持集体动态.
- 该研究强调了机器学习在提高复杂系统的弹性方面的潜力.
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