论文标题
数据驱动的近似和从矩阵铅笔框架中的嘈杂数据减少
Data-driven approximation and reduction from noisy data in matrix pencil frameworks
论文作者
论文摘要
这项工作旨在通过基于矩阵的铅笔技术（即Hankel and Loewner框架）从嘈杂的时间域数据中学习替代模型的问题。提出了一种数据驱动的方法，以从线性时间传播（LTI）系统的时间域输入输入测量值获得降低的状态空间模型。这是通过将上述模型订单降低（MOR）技术与信号矩阵模型（SMM）方法相结合来实现的。提出的方法由由建筑模型组成的数值基准示例说明。
This work aims at tackling the problem of learning surrogate models from noisy time-domain data by means of matrix pencil-based techniques, namely the Hankel and Loewner frameworks. A data-driven approach to obtain reduced-order state-space models from time-domain input-output measurements for linear time-invariant (LTI) systems is proposed. This is accomplished by combining the aforementioned model order reduction (MOR) techniques with the signal matrix model (SMM) approach. The proposed method is illustrated by a numerical benchmark example consisting of a building model.
