000 03839nam a22003975i 4500
001 291757
003 MX-SnUAN
005 20170705134224.0
007 cr nn 008mamaa
008 150903s2005 xxk| o |||| 0|eng d
020 _a9781846281211
_99781846281211
024 7 _a10.1007/b138890
_2doi
035 _avtls000343665
039 9 _a201509031103
_bVLOAD
_c201405070515
_dVLOAD
_y201402061201
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTK5102.9
100 1 _aZaknich, Anthony.
_eautor
_9323197
245 1 0 _aPrinciples of Adaptive Filters and Self-learning Systems /
_cby Anthony Zaknich ; edited by Michael J. Grimble, Michael A. Johnson.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _axxii, 386 páginas 95 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 _aAdvanced Textbooks in Control and Signal Processing,
_x1439-2232
500 _aSpringer eBooks
505 0 _aAdaptive Filtering -- Linear Systems and Stochastic Processes -- Modelling -- Optimisation and Least Squares Estimation -- Parametric Signal and System Modelling -- Classical Filters and Spectral Analysis -- Optimum Wiener Filter -- Optimum Kalman Filter -- Power Spectral Density Analysis -- Adaptive Filter Theory -- Adaptive Finite Impulse Response Filters -- Frequency Domain Adaptive Filters -- Adaptive Volterra Filters -- Adaptive Control Systems -- Nonclassical Adaptive Systems -- to Neural Networks -- to Fuzzy Logic Systems -- to Genetic Algorithms -- Adaptive Filter Application -- Applications of Adaptive Signal Processing -- Generic Adaptive Filter Structures.
520 _aKalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book How can a signal be processed for which there are few or no a priori data? Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing applications. Important topics are introduced and discussed sufficiently to give the reader adequate background for confident further investigation. The material is presented in a progression from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications. Features: • Comprehensive review of linear and stochastic theory. • Design guide for practical application of the least squares estimation method and Kalman filters. • Study of classical adaptive systems together with neural networks, genetic algorithms and fuzzy logic systems and their combination to deal with such complex problems as underwater acoustic signal processing. • Tutorial problems and exercises which identify the significant points and demonstrate the practical relevance of the theory. • PDF Solutions Manual, available to tutors from springeronline.com, containing not just answers to the tutorial problems but also course outlines, sample examination material and project assignments to help in developing a teaching programme and to give ideas for practical investigations.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aGrimble, Michael J.
_eeditor.
_9323198
700 1 _aJohnson, Michael A.
_eeditor.
_9322837
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781852339845
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b138890
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c291757
_d291757