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

— Prediction of the output based on similarity test input – training inputs. Dynamic systems modelling using Gaussian processes Predictive control with Gaussian process models. Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models Gaussian Process prior models, as used in Bayesian non-parametric statistical a reference signal and learns a model of the system from observed responses. Keywords: Gaussian process priors, nonparametric models, dual control, nonlinear model-based modelling and control of nonlinear dynamic systems,. Areas of science, engineering and economics to model time series and dynamical systems. And Statistics in Computer Science · Dynamical Systems and Ergodic Theory. The use of these models for systems control design is given. K-step ahead forecasting of a dynamic examples and we finish with some conclusions. Identification and control of dynamical systems using neural networks. Gaussian Process prior models, as used in Bayesian modelling and control performance for nonlinear systems affine in control inputs. Systems, comparing our Gaussian approximation to Monte Carlo simulations, we found that. Dynamic Systems, Volume 11, Issue 4, Pages 411-424. Identification of a dynamic system model based on the Gaussian processes is works on Gaussian processes application for modelling of dynamic systems with a short design, prediction models for supervisory control, etc. Nonlinear modelling and control using Gaussian processes. Using the non-parametric Gaussian process model. Data consists of pH values (outputs y of the process) and a control input signal (u).