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Document type:
Konferenzbeitrag 
Author(s):
J. Umlauft; A. Lederer; S. Hirche 
Title:
Learning Stable Gaussian Process State Space Models 
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 exploite...    »
 
Keywords:
conhumo; data_driven_control; armin_lederer 
FP7 Projekt ID:
337654 
Book / Congress title:
American Control Conference (ACC) 
Organization:
IEEE 
Publisher:
IEEE 
Year:
2017 
Year / month:
2017-05 
Month:
May 
Pages:
Reviewed:
ja