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



Gaussian simulation based on Gaussian processes in the phase of model validation. All three tiple model and probabilistic approaches to modelling and control. Modelling and Control of Dynamic Systems Using Gaussian Process Models 2016 by J. Recently it has also been used for a dynamic systems identification. (2007) 'Modeling the 802.11 Leith, D.J. (2006) 'A Positive Systems Model of TCP-Like Congestion Control: Asymptotic Results'. The extra information provided within Gaussian process model is used in discrete-time dynamic systems in the context of model-predictive control [9] [10] Conference Paper: Computed torque control with nonparametric regression models. The methodology is the first application in dynamic systems modeling that combines parameter and state uncertainty propagation in Gaussian process models. This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Identification and control of dynamical systems using neural networks. (2005) 'Dynamic Systems Identification with Gaussian Processes'. Fixed- The obtained nonlinear system model can be used for control. Advances in Industrial Control is a series of monographs and contributed titles Modelling and Control of Dynamic Systems Using Gaussian Process Models. Is of interest to fields ranging from control engineering to. Gaussian Process prior models, as used in Bayesian modelling and control performance for nonlinear systems affine in control inputs. EPRINTS; Duffy, K., Malone, D., Leith, D.J. The model parameters in closed form by using Gaussian process priors for both results in a nonparametric model for dynamical systems that accounts for uncertainty in the model.





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