Stability of Gaussian process state space models

2016 European Control Conference (ECC) ◽

10.1109/ecc.2016.7810630 ◽

2016 ◽

Cited By ~ 9

Author(s):

Thomas Beckers ◽

Sandra Hirche

Keyword(s):

Gaussian Process ◽

State Space ◽

State Space Models

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Nonlinear model predictive control with explicit back-offs for Gaussian process state space models

2019 IEEE 58th Conference on Decision and Control (CDC) ◽

10.1109/cdc40024.2019.9029443 ◽

2019 ◽

Author(s):

Eric Bradford ◽

Lars Imsland ◽

Ehecatl Antonio del Rio-Chanona

Keyword(s):

Model Predictive Control ◽

Gaussian Process ◽

State Space ◽

Predictive Control ◽

Nonlinear Model ◽

State Space Models ◽

Nonlinear Model Predictive Control

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On output feedback controller design for Gaussian process state space models

2017 IEEE 56th Annual Conference on Decision and Control (CDC) ◽

10.1109/cdc.2017.8264196 ◽

2017 ◽

Author(s):

Yuji Takaki ◽

Kenji Fujimoto

Keyword(s):

Gaussian Process ◽

State Space ◽

Output Feedback ◽

Controller Design ◽

Feedback Controller ◽

State Space Models ◽

Output Feedback Controller

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Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models

2019 IEEE 58th Conference on Decision and Control (CDC) ◽

10.1109/cdc40024.2019.9029732 ◽

2019 ◽

Author(s):

Karl Berntorp ◽

Kitano Hiroaki

Keyword(s):

Gaussian Process ◽

State Space ◽

Bayesian Learning ◽

State Space Models ◽

Tire Friction

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Recursive Bayesian Inference and Learning of Gaussian-Process State-Space Models

2019 18th European Control Conference (ECC) ◽

10.23919/ecc.2019.8796120 ◽

2019 ◽

Author(s):

Karl Berntorp

Keyword(s):

Bayesian Inference ◽

Gaussian Process ◽

State Space ◽

State Space Models ◽

Recursive Bayesian

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Scenario-based Optimal Control for Gaussian Process State Space Models

2018 European Control Conference (ECC) ◽

10.23919/ecc.2018.8550458 ◽

2018 ◽

Cited By ~ 2

Author(s):

Jonas Umlauft ◽

Thomas Beckers ◽

Sandra Hirche

Keyword(s):

Optimal Control ◽

Gaussian Process ◽

State Space ◽

State Space Models

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Financial time series volatility analysis using Gaussian process state-space models

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) ◽

10.1109/globalsip.2015.7418217 ◽

2015 ◽

Cited By ~ 2

Author(s):

Jianan Han ◽

Xiao-Ping Zhang

Keyword(s):

Time Series ◽

Gaussian Process ◽

State Space ◽

Financial Time Series ◽

State Space Models ◽

Financial Time ◽

Volatility Analysis

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Controllability Gramian of Nonlinear Gaussian Process State Space Models with Application to Model Sparsification

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.215 ◽

2020 ◽

Vol 53 (2) ◽

pp. 443-448

Author(s):

Kenji Kashima ◽

Misaki Imai

Keyword(s):

Gaussian Process ◽

State Space ◽

State Space Models ◽

Controllability Gramian

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Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models

2019 18th European Control Conference (ECC) ◽

10.23919/ecc.2019.8796193 ◽

2019 ◽

Cited By ~ 1

Author(s):

Karl Berntorp

Keyword(s):

Gaussian Process ◽

State Space ◽

State Space Models ◽

Tire Friction

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Online Bayesian inference and learning of Gaussian-process state–space models

Automatica ◽

10.1016/j.automatica.2021.109613 ◽

2021 ◽

Vol 129 ◽

pp. 109613

Author(s):

Karl Berntorp

Keyword(s):

Bayesian Inference ◽

Gaussian Process ◽

State Space ◽

State Space Models

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Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287481 ◽

2021 ◽

Author(s):

Yuhao Liu ◽

Petar M. Djuric

Keyword(s):

Gaussian Process ◽

State Space ◽

State Space Models ◽

Time Varying ◽

Varying Parameters ◽

Time Varying Parameters

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