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Titel:

Learning Stable Gaussian Process State Space Models

Dokumenttyp:
Konferenzbeitrag
Autor(en):
J. Umlauft; A. Lederer; S. Hirche
Abstract:
Data-driven nonparametric models gain importance as control systems are increasingly applied in domains where classical system identification is difficult, e.g., because of the system's complexity, sparse training data or its probabilistic nature. Gaussian process state space models (GP-SSM) are a data-driven approach which requires only high-level prior knowledge like smoothness characteristics. Prior known properties like stability are also often available but rarely exploited duri...     »
Stichworte:
conhumo; data_driven_control; armin_lederer
FP7 Projekt ID:
337654
Kongress- / Buchtitel:
American Control Conference (ACC)
Ausrichter der Konferenz:
IEEE
Verlag / Institution:
IEEE
Jahr:
2017
Jahr / Monat:
2017-05
Monat:
May
Seiten:
6
Reviewed:
ja
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