Modelling and Control of Dynamic Systems Using Gaussian Process Models

Modelling and Control of Dynamic Systems Using Gaussian Process Models
Author :
Publisher : Springer
Total Pages : 281
Release :
ISBN-10 : 9783319210216
ISBN-13 : 3319210211
Rating : 4/5 (211 Downloads)

Book Synopsis Modelling and Control of Dynamic Systems Using Gaussian Process Models by : Juš Kocijan

Download or read book Modelling and Control of Dynamic Systems Using Gaussian Process Models written by Juš Kocijan and published by Springer. This book was released on 2015-11-21 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Modelling and Control of Dynamic Systems Using Gaussian Process Models Related Books

Modelling and Control of Dynamic Systems Using Gaussian Process Models
Language: en
Pages: 281
Authors: Juš Kocijan
Categories: Technology & Engineering
Type: BOOK - Published: 2015-11-21 - Publisher: Springer

DOWNLOAD EBOOK

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 sy
Modeling, Analysis, and Control of Dynamic Systems
Language: en
Pages: 772
Authors: William John Palm
Categories: Science
Type: BOOK - Published: 1983-01-28 - Publisher:

DOWNLOAD EBOOK

An integrated presentation of both classical and modern methods of systems modeling, response and control. Includes coverage of digital control systems. Details
Digital Control Systems Implementation and Computational Techniques
Language: en
Pages: 407
Authors:
Categories: Computers
Type: BOOK - Published: 1996-07-30 - Publisher: Academic Press

DOWNLOAD EBOOK

Praise for the Series:"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the fie
Dynamic Systems And Control With Applications
Language: en
Pages: 468
Authors: Nasir Uddin Ahmed
Categories: Mathematics
Type: BOOK - Published: 2006-08-29 - Publisher: World Scientific Publishing Company

DOWNLOAD EBOOK

In recent years significant applications of systems and control theory have been witnessed in diversed areas such as physical sciences, social sciences, enginee
Estimation and Control of Dynamical Systems
Language: en
Pages: 552
Authors: Alain Bensoussan
Categories: Mathematics
Type: BOOK - Published: 2018-05-23 - Publisher: Springer

DOWNLOAD EBOOK

This book provides a comprehensive presentation of classical and advanced topics in estimation and control of dynamical systems with an emphasis on stochastic c