Deep learning in multi-step prediction of chaotic dynamics [Documento eletrónico] : from deterministic models to real-world systems / by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso
Language: eng - inglesa.Country: Switzerland, Swiss Confederation.Publication: Cham : Springer, 2021Description: XII, 104 p. : il.ISBN: 978-3-030-94482-7.Series: PoliMI SpringerBriefsSubject - Topical Name: Neural networks (Computer science) | Computational intelligence | Artificial intelligence | System theory Online Resources:Click here to access online| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| E-Books | Biblioteca da FCTUNL Online | Não Ficção | QA76.87.SPR FCT (Browse shelf) | 1 | Available | 97334 |
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.
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