Data-driven property verification of grey-box systems by Bayesian experiment design

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A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. These grey-box systems are subject to identification experiments which, new in this contribution, enable accepting or rejecting system properties expressed in a linear-time logic. We employ a Bayesian framework for the computation of a confidence level on the properties and for the design of optimal experiments. Applied to dynamical systems, this work enables data-driven verification of partly-known system dynamics with controllable non-determinism (inputs) and noisy output observations. A numerical case study concerning the safety of a dynamical system is used to elucidate this data-driven and model-based verification technique.

Original languageEnglish
Title of host publicationProceedings of the American Control Conference (ACC2015), 1-3 July 2015, Chicago, USA
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Print)978-1-4799-8684-2
Publication statusPublished - 28 Jul 2015
Event2015 American Control Conference, ACC 2015 - Hilton Palmer House, Chicago, United States
Duration: 1 Jul 20153 Jul 2015


Conference2015 American Control Conference, ACC 2015
Abbreviated titleACC 2015
Country/TerritoryUnited States
Internet address


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