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

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ISBN: 9783319210209 | 267 pages | 7 Mb

• Modelling and Control of Dynamic Systems Using Gaussian Process Models
• Jus Kocijan
• Page: 267
• Format: pdf, ePub, fb2, mobi
• ISBN: 9783319210209
• Publisher: Springer International Publishing

Download Modelling and Control of Dynamic Systems Using Gaussian Process Models

Download ebooks for mobile phones for free Modelling and Control of Dynamic Systems Using Gaussian Process Models

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: · a gas–liquid separator control; · urban-traffic signal modelling and reconstruction; and · prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Cambridge Machine Learning Group Publications
Gaussian processes for data-efficient learning in robotics and control. Bayesian time series learning with Gaussian processes. 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. GP-BayesFilters: Bayesian Filtering Using Gaussian Process
we show how Gaussian process models can be integrated into other Bayes filters observation models for dynamical systems. GPs have been xk−1, uk−1) is the prediction model, a probabilistic model k−1 and control uk−1, using the GP   Gaussian Process for Internal Model Control - CiteSeer
per introduces the use of the Gaussian Process model for Non–linear. Internal Model off–equilibrium dynamics are the main drawbacks of the local modelling techniques. To im- Advances in Supervision and Control Systems. Strunjan  Gaussian Process Model Based Predictive Control - School of
not be compared to linear model based predictive control. This fact is very non- linearities. The use of Gaussian processes in modelling dynamic systems is a. Multiple-step ahead prediction for non linear dynamic systems – A
analysis using the non-parametric Gaussian process model. k-step ahead. forecasting of a discrete-time non-linear dynamic system can be per-. formed by data consists of pH values (outputs y of the process) and a control input signal (u). Modelling and Control of Dynamic Systems Using Gaussian Process
Modelling and Control of Dynamic Systems Using Gaussian Process Models 2016 by J. Kocijan, 9783319210209, available at Book Depository with free  Gaussian process model based predictive control - ResearchGate
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. Modelling and Control of Dynamic Systems Using Gaussian Process
Modelling and Control of Dynamic Systems Using Gaussian Process Models This process of system identification, when based on GP models, can play an  Approximate Methods for Propagation of Uncertainty with Gaussian
This thesis presents extensions of the Gaussian Process (GP) model, based on to derive a control law with a cautious behaviour are addressed. The emphasis is on the use of Gaussian Processes for modelling nonlinear dynamic systems. Nonlinear Predictive Control with a Gaussian Process Model
provided by the Gaussian process model is used in predictive control, The use of Gaussian processes in modelling dynamic systems is a recent development  Derivative observations in Gaussian Process models of dynamic
Derivative observations in Gaussian Process models of dynamic systems (2003) 1137, Fuzzy Identification of Systems and Its Applications to Modeling and Control 210, Prediction with Gaussian processes: from linear regression to linear  Modelling and Control of Dynamic Systems Using - Springer
J. Kocijan. Modelling and Control of Dynamic Systems Using Gaussian Explains how theoretical work in Gaussian process models can be applied in the   nonlinear adaptive control using nonparametric gaussian process
Keywords: Gaussian process priors, nonparametric models, dual control, nonlinear model-based modelling and control of nonlinear dynamic systems,.

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