# Subspace identification of large-scale interconnected systems

arXiv: https://arxiv.org/abs/1309.5105 · HTML: https://mlsanity.com/paper/1309.5105/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2013.

## Built on by (most cited first, 2 of 2)

- [Federated Granger Causality Learning for Interdependent Clients with State Space Representation](https://mlsanity.com/paper/2501.13890/) (2025)
- [Distributed Identification of Contracting and/or Monotone Network Dynamics](https://mlsanity.com/paper/2107.14309/) (2021)

## Abstract (arXiv)

We propose a decentralized subspace algorithm for identification of large-scale, interconnected systems that are described by sparse (multi) banded state-space matrices. First, we prove that the state of a local subsystem can be approximated by a linear combination of inputs and outputs of the local subsystems that are in its neighborhood. Furthermore, we prove that for interconnected systems with well-conditioned, finite-time observability Gramians (or observability matrices), the size of this neighborhood is relatively small. On the basis of these results, we develop a subspace identification algorithm that identifies a state-space model of a local subsystem from the local input-output data. Consequently, the developed algorithm is computationally feasible for interconnected systems with a large number of local subsystems. Numerical results confirm the effectiveness of the new identification algorithm.