DocumentCode :
728203
Title :
Data-driven property verification of grey-box systems by bayesian experiment design
Author :
Haesaert, S. ; Van den Hof, P.M.J. ; Abate, A.
Author_Institution :
Control Syst. Group, Fac. of Electr. Eng., Eindhoven Univ. of Technol., Eindhoven, Netherlands
fYear :
2015
fDate :
1-3 July 2015
Firstpage :
1800
Lastpage :
1805
Abstract :
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.
Keywords :
Bayes methods; formal logic; formal verification; statistical analysis; Bayesian experiment design; confidence level computation; data driven property verification; dynamical system; grey-box systems; linear time logic; measurement-based statistical verification approach; model-based verification technique; optimal experiment design; partly unknown dynamics; Approximation methods; Bayes methods; Computational modeling; Mathematical model; Noise; Noise measurement; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
Type :
conf
DOI :
10.1109/ACC.2015.7170994
Filename :
7170994
Link To Document :
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