國立虎尾科技大學 |

Modelling and Control of Dynamic Systems Using Gaussian Process Models

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modelling and Control of Dynamic Systems Using Gaussian Process Models/ by Juš Kocijan.
作者:	Kocijan, Juš.
面頁冊數:	XVI, 267 p. 117 illus., 17 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Control engineering. -
電子資源:	https://doi.org/10.1007/978-3-319-21021-6
ISBN:	9783319210216

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

Modelling and Control of Dynamic Systems Using Gaussian Process Models[electronic resource] /by Juš Kocijan. - 1st ed. 2016. - XVI, 267 p. 117 illus., 17 illus. in color.online resource. - 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:

1249728
Control engineering.

LC Class. No.: TJ212-225

Dewey Class. No.: 629.8

Modelling and Control of Dynamic Systems Using Gaussian Process Models
LDR:03483nam a22004095i 4500 001 973650
003 DE-He213
005 20200704083540.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319210216 $9 978-3-319-21021-6
024 7 $a 10.1007/978-3-319-21021-6 $2 doi
035 $a 978-3-319-21021-6
050 4 $a TJ212-225
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
082 0 4 $a 629.8 $2 23
100 1 $a Kocijan, Juš. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1268620
245 1 0 $a Modelling and Control of Dynamic Systems Using Gaussian Process Models $h [electronic resource] / $c by Juš Kocijan.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XVI, 267 p. 117 illus., 17 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Advances in Industrial Control, $x 1430-9491
505 0 $a System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.
520 $a 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.
650 0 $a Control engineering. $3 1249728
650 0 $a Chemical engineering. $3 555952
650 0 $a Statistics . $3 1253516
650 1 4 $a Control and Systems Theory. $3 1211358
650 2 4 $a Industrial Chemistry/Chemical Engineering. $3 671153
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319210209
776 0 8 $i Printed edition: $z 9783319210223
776 0 8 $i Printed edition: $z 9783319793276
830 0 $a Advances in Industrial Control, $x 1430-9491 $3 1253663
856 4 0 $u https://doi.org/10.1007/978-3-319-21021-6
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)