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Titel:

Learning Stable Stochastic Nonlinear Dynamical Systems

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
J. Umlauft; S. Hirche
Abstract:
A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e.g., from first principles in physics. However, meta-knowledge on the system’s behavior is often given and should be exploited: Stability as fundamental property is essential when the model is used for controller design or movement generation. Therefore, this paper proposes a framework for learning stable stochastic system...     »
Stichworte:
conhumo; data_driven_control
Kongress- / Buchtitel:
International Conference on Machine Learning (ICML)
Jahr:
2017
Monat:
Aug
Seiten:
9
Reviewed:
ja
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