Gaussian processes for machine learning
D Petelin - International Journal of Neural Systems, 2006 - ipssc.mps.si
Gaussian process (GP) models form a new, emerging complementary method for nonlinear
system identification. The GP model is a probabilistic nonparametric black-box model. It …
system identification. The GP model is a probabilistic nonparametric black-box model. It …
[PDF][PDF] Accelerated learning of Gaussian process models
B Musizza, D Petelin, J Kocijan - … of the 7th EUROSIM Congress on …, 2010 - Citeseer
The Gaussian process model is an example of a flexible, probabilistic, nonparametric model
with uncertainty predictions. It offers a range of advantages for modelling from data and has …
with uncertainty predictions. It offers a range of advantages for modelling from data and has …
[HTML][HTML] Implementation of Gaussian process models for non-linear system identification
KR Thompson - 2009 - theses.gla.ac.uk
This thesis is concerned with investigating the use of Gaussian Process (GP) models for the
identification of nonlinear dynamic systems. The Gaussian Process model is a non …
identification of nonlinear dynamic systems. The Gaussian Process model is a non …
Plenary lecture 1: dynamic GP models: an overview and recent developments
J Kocijan - Proceedings of the 6th international conference on …, 2012 - dl.acm.org
Various methods can be used for nonlinear, dynamic-systems identification and Gaussian
process model is a relatively recentone. The Gaussian-process model is an example of a …
process model is a relatively recentone. The Gaussian-process model is an example of a …
Dynamical systems identification using Gaussian process models with incorporated local models
K Ažman, J Kocijan - Engineering Applications of Artificial Intelligence, 2011 - Elsevier
Gaussian process (GP) models form an emerging methodology for modelling nonlinear
dynamic systems which tries to overcome certain limitations inherent to traditional methods …
dynamic systems which tries to overcome certain limitations inherent to traditional methods …
An empirical evaluation of robust Gaussian process models for system identification
Abstract System identification comprises a number of linear and nonlinear tools for black-
box modeling of dynamical systems, with applications in several areas of engineering …
box modeling of dynamical systems, with applications in several areas of engineering …
[PDF][PDF] The GPML Toolbox Version 3.5
CE Rasmussen, H Nickisch - Url: www. GaussianProcess. org …, 2011 - researchgate.net
The GPML toolbox is an Octave 3.2. x and Matlab 7. x implementation of inference and
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
[PDF][PDF] The gpml toolbox
CE Rasmussen, H Nickisch - ebrary, Dec, 2006 - eie.polyu.edu.hk
The GPML toolbox is an Octave 3.2. x and Matlab 7. x implementation of inference and
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
[PDF][PDF] The GPML toolbox version 4.0
CE Rasmussen, H Nickisch - Technical Documentation, 2016 - gaussianprocess.org
The GPML toolbox is an Octave 3.2. x and Matlab 7. x implementation of inference and
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
prediction in Gaussian process (GP) models. It implements algorithms discussed in …
Use of Gaussian Processes in System Identification
S Särkkä - Encyclopedia of Systems and Control, 2021 - Springer
Gaussian processes are used in machine learning to learn input-output mappings from
observed data. Gaussian process regression is based on imposing a Gaussian process …
observed data. Gaussian process regression is based on imposing a Gaussian process …