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

Modelling and Control of Dynamic Systems Using Gaussian Process Models



Download Modelling and Control of Dynamic Systems Using Gaussian Process Models

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


Dynamic systems modelling using Gaussian processes Predictive control with Gaussian process models. 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. Murray-Smith and Control of Dynamical Systems Using Neural Net- works. Dynamic systems control with GP. (2007) 'Modeling the 802.11 Leith, D.J. The methodology is the first application in dynamic systems modeling that combines parameter and state uncertainty propagation in Gaussian process models. (2006) 'A Positive Systems Model of TCP-Like Congestion Control: Asymptotic Results'. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. €� Model based predictive control. Keywords: Cautious control, Gaussian process priors, nonparametric models, nonlinear model-based metric approach to modelling nonlinear systems. EPRINTS; Duffy, K., Malone, D., Leith, D.J. (2005) 'Dynamic Systems Identification with Gaussian Processes'. –� General model based predictive control.





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