A model learning perspective on the complexity of cyber-physical systems
Publication date
2025-05-27
Document type
Konferenzbeitrag
Author
Swantje Plambeck
Benndorf, Gesa
Organisational unit
Institute of Embedded Systems, Hamburg University of Technology
Fraunhofer Center for Machine Learning, Fraunhofer IOSB-INA
Publisher
Universitätsbibliothek der HSU/UniBw H
Book title
Machine learning for cyber physical systems : proceedings of the conference ML4CPS 2025
First page
59
Last page
68
Part of the university bibliography
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Language
English
Keyword
Model learning
System complexity
Data dimensionality
Hybrid dynamical systems
Hybrid automata
Abstract
A large palette of models and their corresponding learning algorithms have been applied to time series observed from cyber-physical systems (CPSs). For some use cases, simple linear methods are sufficient, while for others, even sophisticated machine learning approaches fail to extract subtle patterns in system behavior. To date, the literature has not examined this phenomenon adequately and lacks a comprehensive analysis linking the characteristics of CPSs with the suitability of different models and learning algorithms.
In this work, after examining the complexity of multiple real-world and artificial CPS use cases, we identify several key aspects that distinguish them: 1) the number of system variables, 2) the degree of interdependence between discrete-event part and continuous part of the system, and 3) the number of unobserved system inputs. By analyzing the approaches successfully applied in the respective use cases, we were able to distill preferred techniques for addressing systems of different complexity levels.
In this work, after examining the complexity of multiple real-world and artificial CPS use cases, we identify several key aspects that distinguish them: 1) the number of system variables, 2) the degree of interdependence between discrete-event part and continuous part of the system, and 3) the number of unobserved system inputs. By analyzing the approaches successfully applied in the respective use cases, we were able to distill preferred techniques for addressing systems of different complexity levels.
Version
Published version
Access right on openHSU
Open access