Predictability of Complex Dynamical Systems. Springer Series in Synergetics, 69

Kravtsov, Yurii A. and James B. Kadtke:

ISBN 10: 3540612874 / ISBN 13: 9783540612872

Verlag: Springer Verlag;, 1996

Beschreibung

234 Seiten Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 490.

Bestandsnummer des Verkäufers 2125554

Bibliografische Details

• Titel: Predictability of Complex Dynamical Systems. Springer Series in Synergetics, 69
• Verlag: Springer Verlag
• Erscheinungsjahr: 1996
• Sprache: Englisch
• ISBN-10: 3540612874
• ISBN-13: 9783540612872
• Einband: gebundene Ausgabe
• Auflage: Auflage: 1.
• Zustand: Gut
• Gewicht: 490 Gramm
• Verkäuferkataloge:
  • 90 Varia

Über diesen Titel

Inhaltsangabe

This book addresses researchers and practitioners interested in modeling, prediction and forecasting of natural systems based on nonlinear dynamics. It is a practical guide to data analysis and to the development of algorithms especially for complex systems presenting topics like characterization of nonlinear correlations in data as dynamical systems, reconstruction of dynamical models from data, nonlinear noise reduction and the limits of predicatability. The authors consider practical problems from e.g. signal and time series analysis, biomedical data analysis, financial analysis, stochastic modeling, human evolution, and political modeling. They give new methods for nonlinear filtering of complex signals and new algorithms for signal classification, and the concept of the "Global Brain".

Von der hinteren Coverseite

This is a book book for researchers and practitioners interested in modeling, prediction and forecasting of natural systems based on nonlinear dynamics. It is a practical guide to data analysis and to the development of algorithms, especially for complex systems. Topics such as the characterization of nonlinear correlations in data as dynamical systems, reconstruction of dynamical models from data, nonlinear noise reduction and the limits of predicatability are discussed. The chapters are written by leading experts and consider practical problems such as signal and time series analysis, biomedical data analysis, financial analysis, stochastic modeling, human evolution, and political modeling. The book includes new methods for nonlinear filtering of complex signals, new algorithms for signal classification, and the concept of the "Global Brain".