Willkommen, schön sind Sie da!
Logo Ex Libris

Deep Learning in Multi-step Prediction of Chaotic Dynamics

  • Kartonierter Einband
  • 116 Seiten
(0) Erste Bewertung abgeben
Bewertungen & Rezensionen
(0)
(0)
(0)
(0)
(0)
Alle Bewertungen ansehen
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series.... Weiterlesen
20%
65.00 CHF 52.00
Sie sparen CHF 13.00
Print on Demand - Auslieferung erfolgt in der Regel innert 4 bis 6 Wochen.
Bestellung & Lieferung in eine Filiale möglich

Beschreibung

The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.



Inhalt

Produktinformationen

Titel: Deep Learning in Multi-step Prediction of Chaotic Dynamics
Untertitel: From Deterministic Models to Real-World Systems
Autor:
EAN: 9783030944810
ISBN: 3030944816
Format: Kartonierter Einband
Herausgeber: Springer International Publishing
Anzahl Seiten: 116
Gewicht: 189g
Größe: H235mm x B155mm x T6mm
Jahr: 2022
Untertitel: Englisch
Auflage: 1st ed. 2021

Weitere Produkte aus der Reihe "SpringerBriefs in Applied Sciences and Technology"