High Performance Computing Applied to Nonlinear Time Series Analysis - Couverture souple

Mar�n Carri�n, Ismael

9783838365879: High Performance Computing Applied to Nonlinear Time Series Analysis

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ISBN 10 : 3838365879 ISBN 13 : 9783838365879

Editeur : LAP LAMBERT Academic Publishing, 2010

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Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification.

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�diteurLAP LAMBERT Academic Publishing
Date d'�dition2010
ISBN 10 3838365879
ISBN 13 9783838365879
ReliureBroch�
Langueanglais
Nombre de pages184

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High Performance Computing Applied to Nonlinear Time Series Analysis

Ismael Mar�n Carri�n

Edit� par LAP LAMBERT Academic Publishing Mai 2010, 2010

ISBN 10 : 3838365879 ISBN 13 : 9783838365879

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification. 184 pp. Englisch. N� de r�f. du vendeur 9783838365879

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High Performance Computing Applied to Nonlinear Time Series Analysis

Ismael Mar�n Carri�n

Edit� par LAP LAMBERT Academic Publishing, 2010

ISBN 10 : 3838365879 ISBN 13 : 9783838365879

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification. N� de r�f. du vendeur 9783838365879

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