000			03698nam a22003975i 4500
001			287455
003			MX-SnUAN
005			20160429154548.0
007			cr nn 008mamaa
008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447143512 _99781447143512
024	7		_a10.1007/9781447143512 _2doi
035			_avtls000339792
039		9	_a201509030319 _bVLOAD _c201404300405 _dVLOAD _y201402060944 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTJ212-225
100	1		_aNúñez, Alfredo A. _eautor _9316654
245	1	0	_aHybrid Predictive Control for Dynamic Transport Problems / _cby Alfredo A. Núñez, Doris A. Sáez, Cristián E. Cortés.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_axIx, 169 páginas 76 ilustraciones, 25 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aAdvances in Industrial Control, _x1430-9491
500			_aSpringer eBooks
505	0		_aHybrid Predictive Control: Mono-objective and Multi-objective Design -- Hybrid Predictive Control for a Dial-a-ride System -- Hybrid Predictive Control for Operational Decisions in Public Transport Systems.
520			_aHybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ?hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO); ?HPC based on EMO for dial-a-ride systems; and ?HPC based on EMO for operational decisions in public transport systems. Hybrid Predictive Control for Dynamic Transport Problems is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here in other process fields will appeal to a wider group of researchers, scientists and graduate students working in other control-related disciplines.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aSáez, Doris A. _eautor _9316655
700	1		_aCortés, Cristián E. _eautor _9316656
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447143505
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4351-2 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c287455 _d287455