國立虎尾科技大學 |

Deep Learning in Multi-step Prediction of Chaotic Dynamics = From Deterministic Models to Real-World Systems /

紀錄類型:	書目-語言資料,印刷品 : 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:	Guariso, Giorgio.
面頁冊數:	XII, 104 p. 46 illus., 25 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Complex Systems. -
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. - 1st ed. 2021. - XII, 104 p. 46 illus., 25 illus. in color.online resource. - PoliMI SpringerBriefs,2282-2585. - 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:

888664
Complex Systems.

LC Class. No.: QA76.87

Dewey Class. No.: 519

Deep Learning in Multi-step Prediction of Chaotic Dynamics = From Deterministic Models to Real-World Systems /
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