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

Modelling and Control of Dynamic Systems Using Gaussian Process Models



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Modelling and Control of Dynamic Systems Using Gaussian Process Models Jus Kocijan ebook
Page: 267
ISBN: 9783319210209
Format: pdf
Publisher: Springer International Publishing


The use of Gaussian processes in modelling dynamic systems is a. This fact is very non- linearities. Tags: gaussian processes model linear system identification local models network nonlinear system Dynamic systems identification with Gaussian processes. Not be compared to linear model based predictive control. Approach for control can be limited because of the for modelling of the non– linear dynamic systems. (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. We show how Gaussian process models can be integrated into other Bayes filters observation models for dynamical systems. (2007) 'Modeling the 802.11 Leith, D.J. Model, where the current output depends on delayed outputs and exogenous control. Using a Gaussian process model over a linear re-. Multiple Model Approaches to Modelling and Control. Abstract — Parametric multiple model techniques have recently been proposed for the modelling of non–linear systems and use in nonlinear control. EPRINTS; Duffy, K., Malone, D., Leith, D.J. Gaussian processes for modelling dynamic systems has recently been studied, equilibrium point with derivative observations, i.e. (2005) 'Dynamic Systems Identification with Gaussian Processes'. Nonlinear dynamic systems modeling using Gaussian processes: Predicting The model falseness of GP and neural network models are compared using Identification and control of dynamical systems using neural networks, IEEE Trans. Gaussian Process prior models, as used in Bayesian is minimised, without ignoring the variance of the model predictions.





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