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Data-driven and model-based verification via Bayesian identification and reachability analysis
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DOI：10.1016/j.automatica.2017.01.037.png)
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Abstract
En 中文
This work develops a measurement-driven and model-based formal verification approach, applicable to dynamical systems with partly unknown dynamics. We provide a new principled method, grounded on 1 Bayesian inference and on reachability analysis respectively, to compute the confidence that a physical system driven by external inputs and accessed under noisy measurements verifies a given property expressed as a temporal logic formula. A case study discusses the bounded- and unbounded-time safety verification of a partly unknown system, encompassed within a class of linear, time-invariant dynamical models with inputs and output measurements. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Temporal logic properties
Bayesian inference
Linear time-invariant models
Model-based verification
Reachability analysis
Data-driven validation
Statistical model checking
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