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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We consider stability analysis of constrained switching linear systems inwhich the dynamics is unknown and whose switching signal is constrained by anautomaton. We propose a data-driven Lyapunov framework for providingprobabilistic stability guarantees based on data harvested from observations ofthe system. By generalizing previous results on arbitrary switching linearsystems, we show that, by sampling a finite number of observations, we are ableto construct an approximate Lyapunov function for the underlying system.Moreover, we show that the entropy of the language accepted by the automatonallows to bound the number of samples needed in order to reach somepre-specified accuracy.

Learning stability guarantees for data-driven constrained switching linear systems