Modelling and control of dynamic systems using Gaussian process models [electronic resource] / Juš Kocijan.
2016
QA274.4
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Details
Title
Modelling and control of dynamic systems using Gaussian process models [electronic resource] / Juš Kocijan.
Author
ISBN
9783319210216 (electronic book)
3319210211 (electronic book)
9783319210209
3319210211 (electronic book)
9783319210209
Published
Cham : Springer, 2016.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-319-21021-6 doi
Call Number
QA274.4
Dewey Decimal Classification
519.2
Summary
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. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (viewed January 13, 2016).
Series
Advances in industrial control.
Available in Other Form
Print version: 9783319210209
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Table of Contents
System Identification with GP Models
Incorporation of Prior Knowledge
Control with GP Models
Trends, Challenges and Research Opportunities
Case Studies.
Incorporation of Prior Knowledge
Control with GP Models
Trends, Challenges and Research Opportunities
Case Studies.