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008			151121s2016 gw | s |||| 0|eng d
020			_a9783319210216
040			_aES-MaUEC
050		4	_aQA274.4 _bK635 2016
082	0	4	_a629.8
100	1		_aKocijan, Juš _997119 _0Local
245	1	0	_aModelling and Control of Dynamic Systems Using Gaussian Process Models _cby Juš Kocijan
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVI, 267 páginas) _b117 ilustraciones, 17 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_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	3		_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	7	_aIngeniería química _2embne _9492897
650	0	7	_aEstadística _2embne _9138934
710	2		_aSpringerLink (Online service) _0Local _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-21021-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319210216
907			_a.b12940033 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aQA274.4 K635 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1158449x _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0