Dynamics-Based Identification of Hybrid Systems using Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{Plambeck:2024:SEAA,

• author = "Swantje Plambeck and Maximilian Schmidt and Goerschwin Fey and Audine Subias and Louise Trave-Massuyes",
• title = "Dynamics-Based Identification of Hybrid Systems using Symbolic Regression",
• booktitle = "2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)",
• year = "2024",
• pages = "64--71",
• month = aug,
• keywords = "genetic algorithms, genetic programming, Fault diagnosis, Accuracy, Biological system modelling, Predictive models, Mathematical models, System identification, Windows, Trajectory, Stakeholders, Software engineering, Hybrid Systems, Symbolic Regression",
• ISSN = "2376-9521",
• DOI = "doi:10.1109/SEAA64295.2024.00019",
• abstract = "Symbolic regression has shown potential in the identification of physical systems. Hybrid systems, which combine both continuous and discrete behaviour, are a relevant extension of purely physical systems, used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper presents a novel approach to learning models of hybrid systems using symbolic regression. Our method leverages the power of genetic programming to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. Symbolic regression detects transitions between different continuous behaviour of a system directly based on the dynamics, instead of pure distances of observed trajectories. Furthermore, models generated by symbolic regression can be used to predict future system behaviour, detect anomalies, and identify the underlying dynamics of the system while providing a human-readable representation. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real system represented in a hybrid model, providing a valuable tool for system identification and diagnosis.",
• notes = "Also known as \cite{10803420}",

}

Genetic Programming entries for Swantje Plambeck Maximilian Schmidt Goerschwin Fey Audine Subias Louise Trave-Massuyes

Citations