论文标题
通过插值降低数据驱动模型的信息
Informativity for data-driven model reduction through interpolation
论文作者
论文摘要
该扩展摘要提出了一种数据驱动插值模型还原的方法。该框架仅根据时间域输入输出数据来计算给定插值点的传输函数值，而无需明确识别高阶系统。取而代之的是，通过表征所有解释数据的系统集，给出了必要和充分的条件，在此集合中的所有系统在给定的插值点共享相同的传输函数值。遵循了所谓的数据信息的观点之后，可以通过经典的插值技术获得降低的模型。电路的一个示例说明了此框架。
A method for data-driven interpolatory model reduction is presented in this extended abstract. This framework enables the computation of the transfer function values at given interpolation points based on time-domain input-output data only, without explicitly identifying the high-order system. Instead, by characterizing the set of all systems explaining the data, necessary and sufficient conditions are given under which all systems in this set share the same transfer function value at a given interpolation point. After following this so-called data informativity perspective, reduced-order models can be obtained by classical interpolation techniques. An example of an electrical circuit illustrates this framework.
