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Deep learning in multi-step predicti...

Sangiorgio, Matteo.

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Deep learning in multi-step prediction of chaotic dynamics = from deterministic models to real-world systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning in multi-step prediction of chaotic dynamics/ by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
其他題名:	from deterministic models to real-world systems /
作者:	Sangiorgio, Matteo.
其他作者:	Dercole, Fabio.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	1 online resource (xii, 104 p.) :ill. (some col.), digital ;24 cm.
內容註:	Introduction to chaotic dynamics' forecasting,. Basic concepts of chaos theory and nonlinear time-series analysis -- Artificial and real-world chaotic oscillators -- Neural approaches for time series forecasting -- Neural predictors' accuracy -- Neural predictors' sensitivity and robustness -- Concluding remarks on chaotic dynamics' forecasting.
Contained By:	Springer Nature eBook
標題:	Chaotic behavior in systems - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-3-030-94482-7
ISBN:	9783030944827

Deep learning in multi-step prediction of chaotic dynamics = from deterministic models to real-world systems /
Sangiorgio, Matteo.

Deep learning in multi-step prediction of chaotic dynamicsfrom deterministic models to real-world systems /[electronic resource] :by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso. - Cham :Springer International Publishing :2021. - 1 online resource (xii, 104 p.) :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology. PoliMI SpringerBriefs,2282-2585. - SpringerBriefs in applied sciences and technology.PoliMI SpringerBriefs..

Introduction to chaotic dynamics' forecasting,. Basic concepts of chaos theory and nonlinear time-series analysis -- Artificial and real-world chaotic oscillators -- Neural approaches for time series forecasting -- Neural predictors' accuracy -- Neural predictors' sensitivity and robustness -- Concluding remarks on chaotic dynamics' forecasting.

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.

ISBN: 9783030944827

Standard No.: 10.1007/978-3-030-94482-7doiSubjects--Topical Terms:

858820
Chaotic behavior in systems
--Mathematical models.

LC Class. No.: Q172.5.C45 / S36 2021

Dewey Class. No.: 003.857015118

Deep learning in multi-step prediction of chaotic dynamics = from deterministic models to real-world systems /
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