000 04127nam a22005655i 4500
001 978-3-319-21021-6
003 DE-He213
005 20220801220733.0
007 cr nn 008mamaa
008 151121s2016 sz | s |||| 0|eng d
020 _a9783319210216
_9978-3-319-21021-6
024 7 _a10.1007/978-3-319-21021-6
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aGPFC
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTJFM
_2thema
082 0 4 _a629.8312
_223
082 0 4 _a003
_223
100 1 _aKocijan, Juš.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_952065
245 1 0 _aModelling and Control of Dynamic Systems Using Gaussian Process Models
_h[electronic resource] /
_cby Juš Kocijan.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVI, 267 p. 117 illus., 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Industrial Control,
_x2193-1577
505 0 _aSystem Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.
520 _aThis 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 _aControl engineering.
_931970
650 0 _aChemistry, Technical.
_914638
650 0 _aStatistics .
_931616
650 1 4 _aControl and Systems Theory.
_931972
650 2 4 _aIndustrial Chemistry.
_914640
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
710 2 _aSpringerLink (Online service)
_952066
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319210209
776 0 8 _iPrinted edition:
_z9783319210223
776 0 8 _iPrinted edition:
_z9783319793276
830 0 _aAdvances in Industrial Control,
_x2193-1577
_952067
856 4 0 _uhttps://doi.org/10.1007/978-3-319-21021-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78882
_d78882