用数据驱动的控制振荡器网络与人口层面的测量
Minh Vu1, Bharat Singhal1, Shen Zeng1
1Department of Electrical and Systems Engineering, Washington University in St Louis, St Louis, Missouri 63130, USA.
Chaos (Woodbury, N.Y.)
|March 25, 2024
概括
这项研究引入了一种新的数据驱动方法,用于控制复杂的振荡器网络. 该技术在线学习控制策略,需要最小的数据和没有先前的系统模型来有效调节同步.
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.
- 网络科学 网络科学
背景情况:
- 控制非线性极限周期振荡器的复杂网络对于工程和自然科学至关重要.
- 现有的方法通常依赖于详细的生物物理或简化阶段模型.
- 没有先前模型假设的数据驱动控制是一个不太发达的研究领域.
研究的目的:
- 直接从数据中开发一个在线学习控制策略,用于振荡器网络.
- 为了应对没有先前存在的模型或广泛的数据集的控制系统的挑战.
- 为了使不同振荡器网络配置中的同步有效调节.
主要方法:
- 利用当前的网络动态进行代的在线控制学习.
- 采用单个输入和一个杂的人口水平输出测量.
- 使用数值模拟进行技术验证.
主要成果:
- 拟议的技术有效调节各种振荡器网络中的同步.
- 在少量试验后才能取得成功的控制.
- 该方法证明了对系统变化的稳定性.
结论:
- 开发的在线学习方法提供了一种强大的数据驱动方法来控制复杂的振荡器网络.
- 这种技术绕过了全球系统模型的需求,简化了控制实施.
- 该方法在需要适应性和高效的网络控制的应用中显示出前景.
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