English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Learning a dynamical system from input/output data is a fundamental task inthe control design pipeline. In the partially observed setting there are twocomponents to identification: parameter estimation to learn the Markovparameters, and system realization to obtain a state space model. In bothsub-problems it is implicitly assumed that standard numerical algorithms suchas the singular value decomposition (SVD) can be easily and reliably computed.When trying to fit a high-dimensional model to data, for example in thecyber-physical system setting, even computing an SVD is intractable. In thiswork we show that an approximate matrix factorization obtained using randomizedmethods can replace the standard SVD in the realization algorithm whilemaintaining the non-asymptotic (in data-set size) performance and robustnessguarantees of classical methods. Numerical examples illustrate that for largesystem models, this is the only method capable of producing a model.

Large-Scale System Identification Using a Randomized SVD