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Title:

Confidence Regions for Simulations with Learned Probabilistic Models

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
A. Lederer; Q. Hao; S. Hirche
Abstract:
Due to the growing amount of data and processing capabilities, machine learning techniques are increasingly applied for the identification of dynamical systems. Especially probabilistic methods are promising for learning models, which in turn are frequently used for simulations. Although confidence regions around predicted trajectories are of crucial importance in many control approaches, few rigorous mathematical analysis methods are available for learned probabilistic models. Therefore, we pro...     »
Keywords:
data_driven_control
Book / Congress title:
Proceedings of the American Control Conference (ACC)
Year:
2020
Year / month:
2020-07
Pages:
3947-3952
Fulltext / DOI:
doi:10.23919/ACC45564.2020.9147978
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