A greedy data collection scheme for linear dynamical systems

被引：7

作者：

Cherifi, Karim ^{[1
]}

Goyal, Pawan ^{[2
]}

Benner, Peter ^{[2
]}

机构：

[1] TU Berlin, Inst Math, MA 4-5,Str 17 Juni 136, D-10623 Berlin, Germany

[2] Max Planck Inst Dynam Complex Tech Syst, Sandtorstr 1, D-39106 Magdeburg, Germany

来源：

DATA-CENTRIC ENGINEERING | 2022年 / 3卷 / 02期

关键词：

Dynamical systems; Loewner framework; machine learning; system identification; MODEL-REDUCTION;

D O I：

10.1017/dce.2022.16

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mathematical models are essential to analyze and understand the dynamics of complex systems. Recently, data-driven methodologies have gotten a lot of attention which is leveraged by advancements in sensor technology. However, the quality of obtained data plays a vital role in learning a good and reliable model. Therefore, in this paper, we propose an efficient heuristic methodology to collect data both in the frequency domain and the time domain, aiming at having more information gained from limited experimental data than equidistant points. In the frequency domain, the interpolation points are restricted to the imaginary axis as the transfer function can be estimated easily on the imaginary axis. The efficiency of the proposed methodology is illustrated by means of several examples, and its robustness in the presence of noisy data is shown.

引用

页数：14

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