Fast Randomized Subspace System Identification for Large I/O Data

TL;DR

This paper introduces a fast randomized subspace system identification method that efficiently handles large-scale input-output data for high-order systems, outperforming traditional algorithms in speed and memory usage.

Contribution

It proposes a novel randomized algorithm for subspace system identification that efficiently manages large datasets and high-order systems, surpassing existing methods in computational efficiency.

Findings

Outperforms traditional methods like N4SID and MOESP in speed and memory efficiency.

Effectively identifies high-order and multi-scale systems with large data samples.

Validated through theoretical analysis and real/simulated case studies.

Abstract

In this article, a novel fast randomized subspace system identification method for estimating combined deterministic-stochastic LTI state-space models, is proposed. The algorithm is especially well-suited to identify high-order and multi-scale systems with both fast and slow dynamics, which typically require a large number of input-output data samples for accurate identification using traditional subspace methods. Instead of working with such large matrices, the dataset is compressed using randomized methods, which preserve the range-spaces of these matrices almost surely. A novel identification algorithm using this compressed dataset, is proposed. This method enables the handling of extremely large datasets, which often make conventional algorithms like N4SID, MOESP, etc. run out of computer memory. Moreover the proposed method outperforms these algorithms in terms of memory-cost, data-movement, flop count and computation time for cases where these algorithms still work in-spite of large data sizes. The effectiveness of the proposed algorithm is established by theoretical analysis and various real and simulated case studies.