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008			190101s2019 gw a o |||| 0|eng d
020			_a9783030036614 _9
024	7		_a10.1007/978-3-030-03661-4 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTJ213 _b2019 EB
100	1		_aTan, Ai Hui. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aIndustrial Process Identification : _bPerturbation Signal Design and Applications _cby Ai Hui Tan, Keith Richard Godfrey.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019.
300			_a1 recurso en línea (XXI, 217 páginas) _b 130 ilustraciones,14 ilustraciones a color
347			_atext file _bPDF
490	0		_aAdvances in Industrial Control _x1430-9491
505	0		_aIntroduction -- Design of Pseudo-Random Signals for Linear System Identification -- Design of Computer-Optimized Signals for Linear System Identification -- Signal Design for Multi-input System Identification -- Signal Design for the Identification of Nonlinear and Time-Varying Systems -- Case Study on the Identification of Direction-Dependent Electronic Nose System -- Case Study on the Identification of a Multivariable Cooling System with Time-Varying Delay -- Software for Signal Design.
520	3		_aIndustrial Process Identification brings together the latest advances in perturbation signal design. It describes the approaches to the design process that are relevant to industries. The authors' discussion of several software packages (Frequency Domain System Identification Toolbox, prs, GALOIS, multilev_new, and Input-Signal-Creator) will allow readers to understand the different designs in industries and begin designing common classes of signals. The authors include two case studies that provide a balance between the theory and practice of these designs: the identification of a direction-dependent electronic nose system; and the identification of a multivariable cooling system with time-varying delay. Major aspects of signal design such as the formulation of suitable specifications in the face of practical constraints, the classes of designs available, the various objectives necessitating separate treatments when dealing with nonlinear systems, and extension to multi-input scenarios, are discussed. Codes, including some that will produce simulated data, are included to help readers replicate the results described. Industrial Process Identification is a powerful source of information for control engineers working in the process and communications industries seeking guidance on choosing identification software tools for use in practical experiments and case studies. The book will also be of interest to academic researchers and students working in electrical, mechanical and communications engineering and the application of perturbation signal design. Advances in Industrial Control reports and encourages 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		7	_aControl automático _2embne _9405125
700	1		_aGodfrey, Keith Richard. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service) _9106996
776	0	8	_iPrinted edition: _z9783030036607
776	0	8	_iPrinted edition: _z9783030036621
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-03661-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _a_alco _a_vill _b09/2019 _cm _dz _ea _feng _ggw _h0
999			_c111246 _d111246 _x1