Dynamic GP models: an overview and recent developments (original) (raw)

Dynamic systems identification with Gaussian processes

2005

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems.

Gaussian process models for systems identification

Different models can be used for nonlinear dy-namic systems identification and the Gaussian process model is a relatively new option with several interesting features: model predictions contain the measure of confidence, the model has a small number of training parameters and facilitated structure determination, and different possibilities of including prior knowledge exist. In this paper the framework for the identification of a dynamic system model based on the Gaussian processes is presented and a short survey with a comprehensive bibliography of published works on application of Gaussian processes for modelling of dynamic systems is given.

Implementation of Gaussian process models for nonlinear system identification

'Proceedings 5th MATHMOD, Vienna, February 2006', Editors I. Troch, F. Breitenecker, 2006

Abstract 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 nonparametric approach to system identification where the model of the underlying system is to be identified through the application of Bayesian analysis to empirical data. The GP modelling approach has been proposed as an alternative to more conventional methods of system identification due to a number of attractive features.

Plenary lecture 1: dynamic GP models: an overview and recent developments

2012

Various methods can be used for nonlinear, dynamic-system identification and Gaussian process (GP) model is a relatively recent one. The GP model is an example of a probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a facilitated structure determination and different possibilities of including prior knowledge about the modelled system. The framework for the identification of dynamic systems with GP models are presented and an overview of recent advances in the research of dynamic-system identification with GP models and its applications are given.

Identification of continuous-time nonlinear systems by using a gaussian process model

IEEJ Transactions on Electrical and Electronic Engineering, 2008

This paper deals with a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process model. Genetic algorithm is applied to train the Gaussian process prior model by minimizing the negative log marginal likelihood of the identification data. The nonlinear term of the objective system is estimated as the predictive mean function of the Gaussian process, and the confidence measure of the estimated nonlinear function is given by the predictive covariance function of the Gaussian process.

The concept for Gaussian process model based system identification toolbox

Proceedings of the 2007 international conference on Computer systems and technologies - CompSysTech '07, 2007

The Gaussian process model is an example of a flexible, probabilistic, nonparametric model with uncertainty predictions. It can be used for the modelling of complex nonlinear systems and recently it has also been used for dynamic systems identification. A need for the supporting software, in particular for dynamic system identification, has been recognised. Consequently, a Matlab toolbox concept for Gaussian Process based System Identification was generated. The use of the supporting software is illustrated with a nonlinear dynamic system identification example.

An example of Gaussian process model identification

The paper describes the identification of nonlinear dynamic systems with a Gaussian process prior model. This approach is an example of a probabilistic, non-parametric modelling. Gaussian process model can be considered as the special case of radial basis function network and as such an alternative to neural networks or fuzzy black box models. An attractive feature of Gaussian process model is that the variance, associated with the model response, is readily obtained. Variance can be seen as uncertainty of the model and can be used to obtain more accurate multi-step ahead prediction. The method is demonstrated on laboratory pilot plant identification.

Incorporating knowledge about model structure in the identification of Gaussian-process models

2013

Dynamic system identification with Gaussian-process prior model is a probabilistic, nonparametric modelling method for identification. Gaussian-process models provide, besides the prediction, also the information about prediction uncertainty based on the availability or uncertainty of the data used for the modelling. An advantage of this kind of model is a small number of training parameters, a facilitated structure determination and the possibility to include various sorts of prior knowledge into the model. One of possibilities is to include blockstructure knowledge like Hammerstein model structure. The identification procedure of Gaussian-process model with Hammerstein model structure will be presented and illustrated with an example. Key–Words: System identification, Gaussian process models, dynamic systems, Hammerstein model.

Modelling and Control of Dynamic Systems Using Gaussian Process Models

2016

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Highlights: - Explains how theoretical work in Gaussian process models can be applied in the control of real industrial systems - Provides the engineer with practical guidance is not unduly encumbered by complicated theory - Shows the academic researcher the potential for real-world application of a recent branch of control theory More information about the book on the Springer web page (http://www.springer.com/us/book/9783319210209).