001441875 000__ 03567cam\a2200565Ii\4500
001441875 001__ 1441875
001441875 003__ OCoLC
001441875 005__ 20230309003347.0
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001441875 007__ cr\cn\nnnunnun
001441875 008__ 220219s2021\\\\sz\a\\\\ob\\\\001\0\eng\d
001441875 019__ $$a1297069492$$a1297828088
001441875 020__ $$a9783030944827$$q(electronic bk.)
001441875 020__ $$a3030944824$$q(electronic bk.)
001441875 020__ $$z9783030944810
001441875 020__ $$z3030944816
001441875 0247_ $$a10.1007/978-3-030-94482-7$$2doi
001441875 035__ $$aSP(OCoLC)1298389061
001441875 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dYDX$$dGW5XE$$dOCLCO$$dOCLCF$$dOCLCQ
001441875 049__ $$aISEA
001441875 050_4 $$aQ172.5.C45$$bS36 2021
001441875 08204 $$a003/.857015118$$223
001441875 1001_ $$aSangiorgio, Matteo,$$eauthor.
001441875 24510 $$aDeep learning in multi-step prediction of chaotic dynamics :$$bfrom deterministic models to real-world systems /$$cMatteo Sangiorgio, Fabio Dercole, Giorgio Guariso.
001441875 264_1 $$aCham :$$bSpringer,$$c[2021]
001441875 264_4 $$c©2021
001441875 300__ $$a1 online resource (111 pages) :$$billustrations (some color).
001441875 336__ $$atext$$btxt$$2rdacontent
001441875 337__ $$acomputer$$bc$$2rdamedia
001441875 338__ $$aonline resource$$bcr$$2rdacarrier
001441875 4901_ $$aSpringerBriefs in applied sciences and technology. PoliMI SpringerBriefs
001441875 504__ $$aIncludes bibliographical references and index.
001441875 5050_ $$aIntroduction to chaotic dynamics forecasting,. Basic concepts of chaos theory and nonlinear time-series analysis -- Artificial and real-world chaotic oscillators -- Neural approaches for time series forecasting -- Neural predictors accuracy -- Neural predictors sensitivity and robustness -- Concluding remarks on chaotic dynamics forecasting.
001441875 506__ $$aAccess limited to authorized users.
001441875 520__ $$aThe book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation.
001441875 588__ $$aDescription based upon print version of record.
001441875 650_0 $$aChaotic behavior in systems$$xMathematical models.
001441875 650_0 $$aDeep learning (Machine learning)
001441875 650_6 $$aChaos$$xModèles mathématiques.
001441875 655_0 $$aElectronic books.
001441875 7001_ $$aDercole, Fabio,$$eauthor.
001441875 7001_ $$aGuariso, Giorgio,$$eauthor.
001441875 77608 $$iPrint version:$$aSangiorgio, Matteo$$tDeep Learning in Multi-Step Prediction of Chaotic Dynamics$$dCham : Springer International Publishing AG,c2022$$z9783030944810
001441875 830_0 $$aSpringerBriefs in applied sciences and technology.$$pPoliMI SpringerBriefs.
001441875 852__ $$bebk
001441875 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-94482-7$$zOnline Access$$91397441.1
001441875 909CO $$ooai:library.usi.edu:1441875$$pGLOBAL_SET
001441875 980__ $$aBIB
001441875 980__ $$aEBOOK
001441875 982__ $$aEbook
001441875 983__ $$aOnline
001441875 994__ $$a92$$bISE