000 04452nam a22005895i 4500
001 978-0-8176-4459-8
003 DE-He213
005 20251006084435.0
007 cr nn 008mamaa
008 100301s2006 xxu| s |||| 0|eng d
020 _a9780817644598
020 _a99780817644598
024 7 _a10.1007/0-8176-4459-8
_2doi
082 0 4 _a624.15
_223
100 1 _aTirozzi, Brunello.
_eauthor.
245 1 0 _aNeural Networks and Sea Time Series
_h[electronic resource] :
_bReconstruction and Extreme-Event Analysis /
_cby Brunello Tirozzi, Silvia Puca, Stefano Pittalis, Antonello Bruschi, Sara Morucci, Enrico Ferraro, Stefano Corsini.
264 1 _aBoston, MA :
_bBirkhäuser Boston,
_c2006.
300 _aX, 179 p. 64 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aModeling and Simulation in Science, Engineering and Technology
505 0 _aBasic Notions on Waves and Tides -- The Wave Amplitude Model -- Artificial Neural Networks -- Approximation Theory -- Extreme-Value Theory -- Application of ANN to Sea Time Series -- Application of Approximation Theory and ARIMA Models -- Extreme-Event Analysis -- Generalization to Other Phenomena -- Conclusions.
520 _aIncreasingly, neural networks are used and implemented in a wide range of fields and have become useful tools in probabilistic analysis and prediction theory. This book-unique in the literature-studies the application of neural networks to the analysis of time series of sea data, namely significant wave heights and sea levels. The particular problem examined as a starting point is the reconstruction of missing data, a general problem that appears in many cases of data analysis. Specific topics covered include: * Presentation of general information on the phenomenology of waves and tides, as well as related technical details of various measuring processes used in the study * Description of the model of wind waves (WAM) used to determine the spectral function of waves and predict the behavior of SWH (significant wave heights); a comparison is made of the reconstruction of SWH time series obtained by means of neural network algorithms versus SWH computed by WAM * Principles of artificial neural networks, approximation theory, and extreme-value theory necessary to understand the main applications of the book * Application of artificial neural networks (ANN) to reconstruct SWH and sea levels (SL) * Comparison of the ANN approach and the approximation operator approach, displaying the advantages of ANN * Examination of extreme-event analysis applied to the time series of sea data in specific locations * Generalizations of ANN to treat analogous problems for other types of phenomena and data This book, a careful blend of theory and applications, is an excellent introduction to the use of ANN, which may encourage readers to try analogous approaches in other important application areas. Researchers, practitioners, and advanced graduate students in neural networks, hydraulic and marine engineering, prediction theory, and data analysis will benefit from the results and novel ideas presented in this useful resource.
650 0 _aENGINEERING.
650 0 _aDISTRIBUTION (PROBABILITY THEORY).
650 0 _aPHYSICS.
650 0 _aMATHEMATICAL STATISTICS.
650 0 _aHYDRAULIC ENGINEERING.
650 1 4 _aENGINEERING.
650 2 4 _aSTRUCTURAL FOUNDATIONS, HYDRAULIC ENGINEERING.
650 2 4 _aPROBABILITY THEORY AND STOCHASTIC PROCESSES.
650 2 4 _aSTATISTICAL THEORY AND METHODS.
650 2 4 _aENGINEERING FLUID DYNAMICS.
650 2 4 _aCOMPLEXITY.
650 2 4 _aMATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS.
700 1 _aPuca, Silvia.
_eauthor.
700 1 _aPittalis, Stefano.
_eauthor.
700 1 _aBruschi, Antonello.
_eauthor.
700 1 _aMorucci, Sara.
_eauthor.
700 1 _aFerraro, Enrico.
_eauthor.
700 1 _aCorsini, Stefano.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780817643478
830 0 _aModeling and Simulation in Science, Engineering and Technology
856 4 0 _uhttp://dx.doi.org/10.1007/0-8176-4459-8
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-ENG
942 _2ddc
_cER
999 _c59652
_d59652