*Search Results* - Learning a Gaussian process approximation of a model predictive controller with guarantees

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Learning a Gaussian process approximation of a model predictive controller with guarantees

Rose, Alexander (Verfasser)

Institute of Electrical and Electronics Engineers$BHerausgebendes Organ. 2023 62nd IEEE Conference on Decision and Control (CDC). - [Erscheinungsort nicht ermittelbar] : IEEE, 2023. - ISBN 9798350301243. - (2023), Seite 4094-4099

*eArticle*

Gaussian process building models and their application in model predictive control

Massa Gray, Francesco (Verfasser)

Stuttgart : IGE, Institut für Gebäudeenergetik, Universität Stuttgart, 2017

*book*

Model-based Calibration of Engine Control Units Using Gaussian Process Regression

Tietze, Nils

2015

*book*

Robust multi-stage nonlinear model predictive control

Lucia, Sergio (Verfasser)

Aachen : Shaker, 2015

*book*

Inexact methods for nonlinear model predictive control: stability, applications, and software

Zanelli, Andrea (Verfasser)

Freiburg : Universität, 2022

*eBook*

Modelling and Control of Dynamic Systems Using Gaussian Process Models

Kocijan, Juš (Verfasser)

1st ed. 2016

Cham : Springer International Publishing, 2016

*eBook*

Intention inference and decision making with hierarchical gaussian process dynamics models

Wang, Zhikun

2013

*book*

Explicit nonlinear model predictive control : theory and applications

Grancharova, Alexandra (Verfasser)

Berlin : Springer, 2012

*book*

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Xie, Rui (Verfasser)

Freiburg : Universität, 2021

*eBook*

Gaussian processes for machine learning

Rasmussen, Carl Edward

Cambridge, Mass. [u.a.] : M I T Press, 2006

*book*

Nonlinear model predictive control : theory and algorithms

Grüne, Lars

London [u.a.] : Springer, 2011

*book*

Gaussian Processes for Machine Learning

Rasmussen, Carl Edward (Verfasser)

Cambridge : The MIT Press, 2005

*eBook*

Nonlinear Predictive Control Using Wiener Models : Computationally Efficient Approaches for Polynomial and Neural Structures

Ławryńczuk, Maciej (Verfasser)

1st ed. 2022

Cham : Springer International Publishing, 2022

*eBook*

Nonlinear Model Predictive Control : Towards New Challenging Applications

Magni, Lalo (Herausgeber)

1st ed. 2009

Berlin, Heidelberg : Springer Berlin Heidelberg, 2009

*eBook*

Probabilistic machine learning : advanced topics

Murphy, Kevin P. (Verfasser)

Cambridge, Massachusetts ; London, England : The MIT Press, [2023]

*book*

Foundations of machine learning

Mohri, Mehryar

Cambridge, Mass. [u.a.] : MIT Press, 2012

*book*

Process Operational Safety and Cybersecurity : A Feedback Control Approach

Wu, Zhe (Verfasser)

1st ed. 2021

Cham : Springer International Publishing, 2021

*eBook*

Probabilistic machine learning : an introduction

Murphy, Kevin P. (Verfasser)

Cambridge : The MIT Press, [2022]

*book*

Process Control : Theory and Applications

Corriou, Jean-Pierre (Verfasser)

2nd ed. 2018

Cham : Springer International Publishing, 2018

*eBook*

Process dynamics and control

Seborg, Dale E. (Verfasser)

4th Edition, EMEA Edition

Hoboken, NJ : John Wiley & Sons, [2017]

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