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008 150903s2012 xxk| o |||| 0|eng d
020 _a9781849965286
_99781849965286
024 7 _a10.1007/9781849965286
_2doi
035 _avtls000344750
039 9 _a201509030425
_bVLOAD
_c201405050311
_dVLOAD
_y201402061305
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTJ212-225
100 1 _aRussell, David W.
_eautor
_9322037
245 1 4 _aThe BOXES Methodology :
_bBlack Box Dynamic Control /
_cby David W. Russell.
264 1 _aLondon :
_bSpringer London,
_c2012.
300 _axxI, 224 páginas 99 ilustraciones, 1 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
500 _aSpringer eBooks
505 0 _aChapter 1: Introduction -- Part I: Learning and Artificial Intelligence (AI) -- Chapter 2: The Game Metaphor -- Chapter 3: Introduction to BOXES -- Chapter 4: Dynamic control as a game -- Part II: The Trolley and Pole -- Chapter 5: Control of a simulated inverted pendulum using the BOXES method -- Chapter 6: The Liverpool experiment -- Chapter 7: Solving the auto-start dilemma -- Part III: Other BOXES applications -- Chapter 8: Continuous system control.-  Chapter 9: Other on/off control case studies -- Chapter 10: Two non-linear applications -- Part IV: Improving the Algorithm -- Chapter 11:  Accelerated learning -- Chapter 12:  Two advising paradigms -- Chapter 13: Evolutionary studies research -- Chapter 14: Conclusions.
520 _aRobust control mechanisms customarily require knowledge of the system’s describing equations which may be of the high order differential type.  In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior.  There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent.  Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Michie and Chambers’ BOXES methodology created a black box system that was designed to control a mechanically unstable system with very little a priori system knowledge, linearization or approximation.  All the method needed was some notion of maximum and minimum values for the state variables and a set of boundaries that divided each variable into an integer state number.  The BOXES Methodology applies the method to a variety of systems including continuous and chaotic dynamic systems, and discusses how it may be possible to create a generic control method that is self organizing and adaptive that learns with the assistance of near neighbouring states. The BOXES Methodology introduces students at the undergraduate and master’s level to black box dynamic system control , and gives lecturers access to background materials that can be used in their courses in support of student research and classroom presentations in novel control systems and real-time applications of artificial intelligence. Designers are provided with a novel method of optimization and controller design when the equations of a system are difficult or unknown. Researchers interested in artificial intelligence (AI) research and models of the brain and practitioners from other areas of biology and technology are given an insight into how AI software can be written and adapted to operate in real-time.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781849965279
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84996-528-6
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c291375
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