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Regression Trees for System Models and Prediction
Publikationstyp
Conference Paper
Publikationsdatum
2022-11
Sprache
English
Institut
First published in
Number in series
3311
Start Page
57
End Page
61
Citation
4th Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis (OVERLAY 2022)
Scopus ID
2-s2.0-85145877736
Publisher
RWTH Aachen
Today's systems are highly complex and often appear as black-box systems because of unknown internal functionalities. Thus, retrieving a model of a system is possible only based on observations of the system. We explore the usage of regression trees to learn a model of a complex system with continuous signals. Our approach for learning a regression tree uses observed inputs and outputs of a system from bounded history. We describe how to construct such a model and analyze the accuracy of predictions. Results show the applicability of regression tree models for continuous systems.
Schlagworte
Continuous Systems
Regression Trees
System Models