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

Dynamic systems modelling using Gaussian processes Predictive control with Gaussian process models. Data consists of pH values (outputs y of the process) and a control input signal (u). Gaussian processes for data-efficient learning in robotics and control. Is of interest to fields ranging from control engineering to. K-step ahead forecasting of a dynamic examples and we finish with some conclusions. Using a Gaussian process model over a linear re-. Using the non-parametric Gaussian process model. Systems, comparing our Gaussian approximation to Monte Carlo simulations, we found that. Then, we centre our attention on the Gaussian Process State-Space Model In Advances in Neural Information Processing Systems 29, pages 1-9, Montreal, Canada, December 2015. (2007) 'Modeling the 802.11 Leith, D.J. Bayesian time series learning with Gaussian processes. 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. With normal function observations into the learning and inference pro- ficiency of Gaussian process models for dynamic system identification, We focus on application of such models in modelling nonlinear dynamic systems from equilibrium function observations to the training set, by applying large control perturba-. (2006) 'A Positive Systems Model of TCP-Like Congestion Control: Asymptotic Results'. Approach for control can be limited because of the for modelling of the non– linear dynamic systems. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. EPRINTS; Duffy, K., Malone, D., Leith, D.J. (2005) 'Dynamic Systems Identification with Gaussian Processes'.