Extracting knowledge from time series : an introduction to nonlinear empirical modeling
This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolu...
Enregistré dans:
| Auteurs principaux: | , |
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| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2010. |
| Collection: | Springer Series in Synergetics
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| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Extracting knowledge from time series, an introduction to nonlinear empirical modeling, Boris P. Bezruchko, Dmitry A. Smirnov, 2010, Heidelberg, Springer, 1 vol. (xxi, 405 pages), Springer complexity, 978-3-642-12600-0 • Extracting knowledge from time series, an introduction to nonlinear empirical modeling, Boris P. Bezruchko, Dmitry A. Smirnov, 2010, Heidelberg, Springer, 1 vol. (xxi, 405 pages), Springer complexity, 978-3-642-12600-0 • Extracting Knowledge From Time Series, Texte imprimé, 9783642264825 • Extracting Knowledge From Time Series, Texte imprimé, 9783642126024 |
| Résumé: | This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject |
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| Description: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783642126017 |
| ISSN: | 2198-333X |
| Accès: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |