Deep Learning in Multi-step Prediction of Chaotic Dynamics

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Kartonierter Einband

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

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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
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