| Autor: |
Soto, Miriam García, Henzinger, Thomas A., Schilling, Christian |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
ATVA 2022 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-031-19992-9_22 |
| Popis: |
We propose an algorithmic approach for synthesizing linear hybrid automata from time-series data. Unlike existing approaches, our approach provides a whole family of models. Each model in the family is guaranteed to capture the input data up to a precision error {\epsilon}, in the following sense: For each time series, the model contains an execution that is {\epsilon}-close to the data points. Our construction allows to effectively choose a model from this family with minimal precision error {\epsilon}. We demonstrate the algorithm's efficiency and its ability to find precise models in two case studies. |
| Databáze: |
arXiv |
| Externí odkaz: |
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