大葉大學圖書館 |

Modelling and control of dynamic systems using Gaussian process models[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.2
書名/作者:	Modelling and control of dynamic systems using Gaussian process models/ by Jus Kocijan.
作者:	Kocijan, Jus.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvi, 267 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Gaussian processes.
標題:	System analysis.
標題:	Control theory.
標題:	Engineering.
標題:	Control.
標題:	Industrial Chemistry/Chemical Engineering.
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
ISBN:	9783319210216
ISBN:	9783319210209
內容註:	System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-21021-6

Modelling and control of dynamic systems using Gaussian process models[electronic resource] /
Kocijan, Jus.

Modelling and control of dynamic systems using Gaussian process models[electronic resource] /by Jus Kocijan. - Cham :Springer International Publishing :2016. - xvi, 267 p. :ill., digital ;24 cm. - Advances in industrial control,1430-9491. - Advances in industrial control..

System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.

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.

ISBN: 9783319210216

Standard No.: 10.1007/978-3-319-21021-6doiSubjects--Topical Terms:

337849
Gaussian processes.

LC Class. No.: QA274.4

Dewey Class. No.: 519.2

Modelling and control of dynamic systems using Gaussian process models[electronic resource] /
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