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003 MX-SnUAN
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008 150903s2005 xxk| o |||| 0|eng d
020 _a9781846281846
_99781846281846
024 7 _a10.1007/1846281849
_2doi
035 _avtls000343716
039 9 _a201509030750
_bVLOAD
_c201404120952
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aPalit, Ajoy K.
_eautor
_9323485
245 1 0 _aComputational Intelligence in Time Series Forecasting :
_bTheory and Engineering Applications /
_cby Ajoy K. Palit, Dobrivoje Popovic.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _axxI, 372 páginas 66 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 _aComputational Intelligence: An Introduction -- Traditional Problem Definition -- Basic Intelligent Computational Technologies -- Neural Networks Approach -- Fuzzy Logic Approach -- Evolutionary Computation -- Hybrid Computational Technologies -- Neuro-fuzzy Approach -- Transparent Fuzzy/Neuro-fuzzy Modelling -- Evolving Neural and Fuzzy Systems -- Adaptive Genetic Algorithms -- Recent Developments -- State of the Art and Development Trends.
520 _aForesight in an engineering enterprise can make the difference between success and failure and can be vital to the effective control of industrial systems. Forecasting the future from accumulated historical data is a tried and tested method in areas such as engineering finance. Applying time series analysis in the on-line milieu of most industrial plants has been more problematic because of the time and computational effort required. The advent of soft computing tools such as the neural network and the genetic algorithm offers a solution. Chapter by chapter, Computational Intelligence in Time Series Forecasting harnesses the power of intelligent technologies individually and in combination. Examples of the particular systems and processes susceptible to each technique are investigated, cultivating a comprehensive exposition of the improvements on offer in quality, model building and predictive control, and the selection of appropriate tools from the plethora available; these include: • forecasting electrical load, chemical reactor behaviour and high-speed-network congestion using fuzzy logic; • prediction of airline passenger patterns and of output data for nonlinear plant with combination neuro-fuzzy networks; • evolutionary modelling and anticipation of stock performance by the use of genetic algorithms. Application-oriented engineers in process control, manufacturing, the production industries and research centres will find much to interest them in Computational Intelligence in Time Series Forecasting and the book is suitable for industrial training purposes. It will also serve as valuable reference material for experimental researchers. 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 _aPopovic, Dobrivoje.
_eautor
_9323486
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781852339487
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-184-9
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c291975
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