Verwandte Artikel zu High Performance Computing Applied to Nonlinear Time...

High Performance Computing Applied to Nonlinear Time Series Analysis - Softcover

 
9783838365879: High Performance Computing Applied to Nonlinear Time Series Analysis

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Reseña del editor

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Gratis für den Versand innerhalb von/der Deutschland

Versandziele, Kosten & Dauer

Suchergebnisse für High Performance Computing Applied to Nonlinear Time...

Foto des Verkäufers

Ismael Marín Carrión
ISBN 10: 3838365879 ISBN 13: 9783838365879
Neu Softcover

Anbieter: moluna, Greven, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Zustand: New. Artikel-Nr. 5416915

Verkäufer kontaktieren

Neu kaufen

EUR 55,21
Währung umrechnen
Versand: Gratis
Innerhalb Deutschlands
Versandziele, Kosten & Dauer

Anzahl: Mehr als 20 verfügbar

In den Warenkorb

Foto des Verkäufers

Ismael Marín Carrión
ISBN 10: 3838365879 ISBN 13: 9783838365879
Neu Taschenbuch

Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland

Verkäuferbewertung 5 von 5 Sternen 5 Sterne, Erfahren Sie mehr über Verkäufer-Bewertungen

Taschenbuch. Zustand: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch. Artikel-Nr. 9783838365879

Verkäufer kontaktieren

Neu kaufen

EUR 68,00
Währung umrechnen
Versand: Gratis
Innerhalb Deutschlands
Versandziele, Kosten & Dauer

Anzahl: 2 verfügbar

In den Warenkorb