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Updated: Jul 17, 2025

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从部分数据中重建,预测和稳定混乱动态

Elise Özalp1, Georgios Margazoglou1, Luca Magri1,2

  • 1Department of Aeronautics, Imperial College London, London SW7 2BX, United Kingdom.

Chaos (Woodbury, N.Y.)
|September 6, 2023
PubMed
概括

使用长短期内存 (LSTM) 网络的数据驱动方法可以从部分观测中重建混乱的系统动态和稳定性. 这些方法准确地预测隐藏变量并推断稳定性质,优于传统方法.

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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
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