论文标题
来自嘈杂流数据的动态系统的在线实时学习：Koopman操作员方法
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach
论文作者
论文摘要
传感和通信方面的最新进展有助于从各种物理系统（例如电力网络，气候系统，生物网络等）中获得高频实时数据。但是，由于数据是由物理传感器记录的，因此所获得的数据自然会被测量噪声损坏。在本文中，我们介绍了一种新颖的算法，用于从嘈杂的时间序列数据中在线实时学习动态系统的算法，该数据采用了强大的Koopman操作员框架来减轻测量噪声的影响。提出的算法具有三个主要优点：a）允许在线实时监视动态系统； b）它获得了基本动力学系统的线性表示，从而使用户能够使用线性系统理论来分析和控制系统； c）比流行的扩展动态模式分解（EDMD）算法的计算快速且较少。我们通过应用该算法来识别范德波尔振荡器，IEEE 68总线系统和范德尔旋转振荡器的环网络来说明所提出的算法的效率。
Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: a) it allows for online real-time monitoring of a dynamical system; b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; c) it is computationally fast and less intensive than the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.
