Data-driven and Model-based Verification: a Bayesian Identification Approach
Systems and Control
2015-09-14 v1
Abstract
This work develops a measurement-driven and model-based formal verification approach, applicable to systems with partly unknown dynamics. We provide a principled method, grounded on reachability analysis and on Bayesian inference, to compute the confidence that a physical system driven by external inputs and accessed under noisy measurements, verifies a temporal logic property. A case study is discussed, where we investigate the bounded- and unbounded-time safety of a partly unknown linear time invariant system.
Cite
@article{arxiv.1509.03347,
title = {Data-driven and Model-based Verification: a Bayesian Identification Approach},
author = {Sofie Haesaert and Paul M. J. Van den Hof and Alessandro Abate},
journal= {arXiv preprint arXiv:1509.03347},
year = {2015}
}