東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Deep learning in multi-step predicti...

Sangiorgio, Matteo.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning in multi-step prediction of chaotic dynamics/ by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
Reminder of title:	from deterministic models to real-world systems /
Author:	Sangiorgio, Matteo.
other author:	Dercole, Fabio.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	1 online resource (xii, 104 p.) :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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
Subject:	Chaotic behavior in systems - Mathematical models. -
Online resource:	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 /
LDR:02535nmm a2200337 a 4500 001 2262404
003 DE-He213
005 20220214073718.0
006 m o d
007 cr nn 008maaau
008 220616s2021 sz s 0 eng d
020 $a 9783030944827 $q (electronic bk.)
020 $a 9783030944810 $q (paper)
024 7 $a 10.1007/978-3-030-94482-7 $2 doi
035 $a 978-3-030-94482-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q172.5.C45 $b S36 2021
072 7 $a PBWH $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
082 0 4 $a 003.857015118 $2 23
090 $a Q172.5.C45 $b S225 2021
100 1 $a Sangiorgio, Matteo. $3 3538844
245 1 0 $a Deep learning in multi-step prediction of chaotic dynamics $h [electronic resource] : $b from deterministic models to real-world systems / $c by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a 1 online resource (xii, 104 p.) : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology. PoliMI SpringerBriefs, $x 2282-2585
505 0 $a 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.
520 $a 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.
650 0 $a Chaotic behavior in systems $x Mathematical models. $3 858820
650 0 $a Deep learning (Machine learning) $3 3538509
650 1 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Complex Systems. $3 1566441
700 1 $a Dercole, Fabio. $3 3538845
700 1 $a Guariso, Giorgio. $3 3218156
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied sciences and technology. $p PoliMI SpringerBriefs. $3 2072542
856 4 0 $u https://doi.org/10.1007/978-3-030-94482-7
950 $a Mathematics and Statistics (SpringerNature-11649)