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

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

(2005) 'Dynamic Systems Identification with Gaussian Processes'. EPRINTS; Duffy, K., Malone, D., Leith, D.J. Forecasting of a discrete-time non-linear dynamic system can be per-. Fixed- The obtained nonlinear system model can be used for control. (2006) 'A Positive Systems Model of TCP-Like Congestion Control: Asymptotic Results'. Formed by data consists of pH values (outputs y of the process) and a control input signal (u). On application of Gaussian processes for modelling of dynamic systems is given. (2007) 'Modeling the 802.11 Leith, D.J. The Gaussian Process model is a nonparametric approach to system identification. To be of potential use in model-predictive or adaptive control implementations. Analysis using the non-parametric Gaussian process model. This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Fixed- Structure Gaussian Process model can be interpreted as linear model The modelling and control design will be illustrated with a simple example. 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.