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.
Benner, P., Himpe, C., & Mitchell, T. (2018). On reduced input-output dynamic mode decomposition. Advances in Computational Mathematics, 44(6), 1751–1768. https://doi.org/10.1007/s10444-018-9592-x