Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Andrea Peruffo; Manuel Mazo

doi:10.1109/LCSYS.2022.3186187

Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Andrea Peruffo^*, Manuel Mazo

^*Corresponding author for this work

Team Manuel Mazo Jr

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

8 Downloads (Pure)

Abstract

We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario optimisation to multiclass SVM algorithms in order to construct a PAC map between the concrete state-space and the inter-sample times. We then build a traffic model applying an l-complete relation and find, in the underlying graph, the cycles of minimum and maximum average weight: these provide lower and upper bounds on the AIST. Numerical benchmarks show the practical applicability of our method, which is compared against model-based state-of-the-art tools.

Original language	English
Pages (from-to)	115-120
Journal	IEEE Control Systems Letters
Volume	7
DOIs	https://doi.org/10.1109/LCSYS.2022.3186187
Publication status	Published - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Automata
Behavioral sciences
Computational modeling
Discrete event systems
Picture archiving and communication systems
Probabilistic logic
Stability analysis
Statistical learning
Support vector machines

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Data-Driven_Abstractions_With_Probabilistic_Guarantees_for_Linear_PETC_SystemsFinal published version, 677 KB

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title = "Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems",

abstract = "We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario optimisation to multiclass SVM algorithms in order to construct a PAC map between the concrete state-space and the inter-sample times. We then build a traffic model applying an l-complete relation and find, in the underlying graph, the cycles of minimum and maximum average weight: these provide lower and upper bounds on the AIST. Numerical benchmarks show the practical applicability of our method, which is compared against model-based state-of-the-art tools.",

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AU - Mazo, Manuel

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario optimisation to multiclass SVM algorithms in order to construct a PAC map between the concrete state-space and the inter-sample times. We then build a traffic model applying an l-complete relation and find, in the underlying graph, the cycles of minimum and maximum average weight: these provide lower and upper bounds on the AIST. Numerical benchmarks show the practical applicability of our method, which is compared against model-based state-of-the-art tools.

AB - We employ the scenario approach to compute probably approximately correct (PAC) bounds on the average inter-sample time (AIST) generated by an unknown PETC system, based on a finite number of samples. We extend the scenario optimisation to multiclass SVM algorithms in order to construct a PAC map between the concrete state-space and the inter-sample times. We then build a traffic model applying an l-complete relation and find, in the underlying graph, the cycles of minimum and maximum average weight: these provide lower and upper bounds on the AIST. Numerical benchmarks show the practical applicability of our method, which is compared against model-based state-of-the-art tools.

KW - Automata

KW - Behavioral sciences

KW - Computational modeling

KW - Discrete event systems

KW - Picture archiving and communication systems

KW - Probabilistic logic

KW - Stability analysis

KW - Statistical learning

KW - Support vector machines

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VL - 7

SP - 115

EP - 120

JO - IEEE Control Systems Letters

JF - IEEE Control Systems Letters

ER -

Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems

Abstract

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Keywords

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