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Modelling and Control of Dynamic Systems Using
Modelling and Control of Dynamic Systems Using

Modelling and Control of Dynamic Systems Using Gaussian Process Models. Jus Kocijan

Modelling and Control of Dynamic Systems Using Gaussian Process Models


Modelling.and.Control.of.Dynamic.Systems.Using.Gaussian.Process.Models.pdf
ISBN: 9783319210209 | 267 pages | 7 Mb


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Modelling and Control of Dynamic Systems Using Gaussian Process Models Jus Kocijan
Publisher: Springer International Publishing



Internal Model off–equilibrium dynamics are the main drawbacks of the local modelling techniques. Systems control design relies on mathematical models and these may be developed from measurement data. The use of Gaussian processes in modelling dynamic systems is a. This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Identification of a dynamic system model based on the Gaussian processes is works on Gaussian processes application for modelling of dynamic systems with a short design, prediction models for supervisory control, etc. This fact is very non- linearities. We show how Gaussian process models can be integrated into other Bayes filters observation models for dynamical systems. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. Keywords: Cautious control, Gaussian process priors, nonparametric models, nonlinear model-based metric approach to modelling nonlinear systems. To im- Advances in Supervision and Control Systems. Murray-Smith and Control of Dynamical Systems Using Neural Net- works. Not be compared to linear model based predictive control. Per introduces the use of the Gaussian Process model for Non–linear. Fixed- The obtained nonlinear system model can be used for control.

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