Køb for 499 kr mere for gratis levering til pakkeshop
499 kr
Pakkeshop
599 kr
Hjemmelevering
Gemte
Log ind
0
Kurv
Kurv
Deep Learning in Multi-step Prediction of Chaotic Dynamics
- From Deterministic Models to Real-World Systems
Engelsk
Bogcover for Deep Learning in Multi-step Prediction of Chaotic Dynamics af Giorgio Guariso, Matteo Sangiorgio, Fabio Dercole, 9783030944810
Specifikationer
Sprog:
Engelsk
Sider:
104
ISBN-13:
9783030944810
Indbinding:
Paperback
ISBN-10:
3030944816
Udg. Dato:
15 feb 2022
Størrelse i cm:
23,5 x 15,5
Oplagsdato:
15 feb 2022

Deep Learning in Multi-step Prediction of Chaotic Dynamics

- From Deterministic Models to Real-World Systems
Engelsk
Paperback 2022
Format:

Bog beskrivelse

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.

... Vis mere

Forlags Vejl. pris
582,49 kr
Hos Booktok
458 kr
spar 21%
Læg i kurv nu
Fri fragt over 499,-
90 dages retur
23 - 25 hverdage

Specifikationer
Sprog:
Engelsk
Sider:
104
ISBN-13:
9783030944810
Indbinding:
Paperback
ISBN-10:
3030944816
Udg. Dato:
15 feb 2022
Størrelse i cm:
23,5 x 15,5
Oplagsdato:
15 feb 2022
Finder produkter...
Kategori sammenhænge