An overview of subspace identification

By S. Qin. Computers and Chemical Engineering, 2006

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Projection-based model reduction: Formulations for physics-based machine learning (2019, methodology): “The eigensystem realization algorithm [23, 24, 25, 26] and finite impulse response system identification [27, 28, 29, 22] are two system identification techniques that seek to learn reduced models from impulse responses of linear time-invariant systems.”
Generalizing Koopman Theory to Allow for Inputs and Control (2016, positive): “In fact, DMD has been shown to be intimately connected to ERA and OKID as well as other subspace identification methods such as the numerical algorithms for subspace state space system identification (N4SID) [38, 48, 36].”
Dynamic Mode Decomposition with Control (2014, methodology): “There are, though, interesting differences similar to those found for ERA/OKID, namely in the choice of the projection scaling (see the earlier discussion concerning the similarity transformation required to compare ERA and DMD) and in using the orthogonal complement of the control data matrix Υ for”