On Reduced Input-Output Dynamic Mode Decomposition
For researchers in system identification and reduced-order modeling, this provides incremental improvements to a known method.
This work improves input-output dynamic mode decomposition for system identification by comparing excitation approaches and introducing an optimization-based stabilization strategy, resulting in more stable reduced-order models.
The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems.