通过机器学习检测网络合动态系统中的干扰
Per Sebastian Skardal1, Juan G Restrepo2
1Department of Mathematics, Trinity College, Hartford, Connecticut 06106, USA.
Chaos (Woodbury, N.Y.)
|October 30, 2023
概括
本研究介绍了一种机器学习方法,用于检测网络系统中未知的干扰. 无模型方法通过先前的系统观测和已知的强迫函数来识别干扰位置和类型.
科学领域:
- 复杂的系统复杂的系统.
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 识别网络合动态系统中的干扰对于许多应用来说至关重要.
- 当前的方法往往需要对干扰或系统动态的了解.
研究的目的:
- 开发一种无模型机器学习方法,用于识别网络系统中的未知干扰.
- 确定各种类型干扰的位置和特性.
主要方法:
- 利用在已知的训练函数下对系统的先前观察.
- 采用机器学习方法,不需要先前了解系统动态或干扰.
主要成果:
- 成功确定了各种线性和非线性干扰的位置和特性.
- 在食物网和神经元活动模型上证明有效.
- 验证了该方法使用各种已知的强迫函数的能力.
结论:
- 拟议的无模型方法有效地识别了网络系统中未知的干扰.
- 该方法具有多功能性,适用于不同的系统类型和干扰特征.
- 讨论了将该方法扩展到大型网络的策略.
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