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Data-driven identification of models for discrete and hybrid systems
Citation Link: https://doi.org/10.15480/882.17166
Publikationstyp
Doctoral Thesis
Date Issued
2026
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2026-02-25
Institute
TORE-DOI
Citation
Shaker 978-3-8191-0720-7: (2026)
ISBN
978-3-8191-0662-0
978-3-8191-0720
This thesis addresses the challenge of identifying interpretable models for cyber-physical systems. Data-driven identification leverages observed data to automatically construct models that capture the underlying behavior of systems. The work explores a progression of methods, ranging from classical automata learning and extensions of decision trees, to novel algorithms for hybrid automata learning. Empirical evaluations demonstrate the effectiveness and complementarity of the proposed methods, showcasing their ability to address a wide range of modeling challenges in discrete and hybrid systems.
Subjects
Cyber-Physical Systems
Model Learning
Decision Trees
Automata Learning
Hybrid Systems
DDC Class
629.8: Control and Feedback Control Systems
006.31: Machine Learning
519: Applied Mathematics, Probabilities
Publication version
publishedVersion
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Name
Plambeck_Swantje_Data-Driven-Identification-of-Models-for-Discrete-and-Hybrid-Systems.pdf
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5.19 MB
Format
Adobe PDF