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020 _a9781846280870
_99781846280870
024 7 _a10.1007/b138626
_2doi
035 _avtls000343636
039 9 _a201509030434
_bVLOAD
_c201405070513
_dVLOAD
_y201402061201
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aEspinosa, Jairo.
_eautor
_9323434
245 1 0 _aFuzzy Logic, Identification and Predictive Control /
_cby Jairo Espinosa, Joos Vandewalle, Vincent Wertz.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _axIx, 263 páginas 138 ilustraciones
_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 _aFuzzy Modeling -- Fuzzy Modeling -- Constructing Fuzzy Models from Input-Output Data -- Fuzzy Modeling with Linguistic Integrity: A Tool for Data Mining -- Nonlinear Identification Using Fuzzy Models -- Fuzzy Control -- Fuzzy Control -- Predictive Control Based on Fuzzy Models -- Robust Nonlinear Predictive Control Using Fuzzy Models -- Conclusions and Future Perspectives.
520 _aThe complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system. Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining. Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student. 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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aVandewalle, Joos.
_eautor
_9323435
700 1 _aWertz, Vincent.
_eautor
_9323436
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781852338282
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b138626
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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