000 | 03812nam a22005055i 4500 | ||
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001 | 978-3-319-21021-6 | ||
003 | DE-He213 | ||
005 | 20200421111204.0 | ||
007 | cr nn 008mamaa | ||
008 | 151121s2016 gw | s |||| 0|eng d | ||
020 |
_a9783319210216 _9978-3-319-21021-6 |
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024 | 7 |
_a10.1007/978-3-319-21021-6 _2doi |
|
050 | 4 | _aTJ212-225 | |
072 | 7 |
_aTJFM _2bicssc |
|
072 | 7 |
_aTEC004000 _2bisacsh |
|
082 | 0 | 4 |
_a629.8 _223 |
100 | 1 |
_aKocijan, Juš. _eauthor. |
|
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 |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aAdvances in Industrial Control, _x1430-9491 |
|
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 | _aEngineering. | |
650 | 0 | _aChemical engineering. | |
650 | 0 | _aStatistics. | |
650 | 0 | _aControl engineering. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aControl. |
650 | 2 | 4 | _aIndustrial Chemistry/Chemical Engineering. |
650 | 2 | 4 | _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319210209 |
830 | 0 |
_aAdvances in Industrial Control, _x1430-9491 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-21021-6 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c54054 _d54054 |