LEADER 03701nam 22006735 450 001 9910544860003321 005 20251113191027.0 010 $a3-030-94482-4 024 7 $a10.1007/978-3-030-94482-7 035 $a(MiAaPQ)EBC6888342 035 $a(Au-PeEL)EBL6888342 035 $a(OCoLC)1298389061 035 $a(CKB)21251233100041 035 $a(PPN)260827223 035 $a(DE-He213)978-3-030-94482-7 035 $a(EXLCZ)9921251233100041 100 $a20220214d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning in Multi-step Prediction of Chaotic Dynamics $eFrom Deterministic Models to Real-World Systems /$fby Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (111 pages) 225 1 $aPoliMI SpringerBriefs,$x2282-2585 311 08$aPrint version: Sangiorgio, Matteo Deep Learning in Multi-Step Prediction of Chaotic Dynamics Cham : Springer International Publishing AG,c2022 9783030944810 327 $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. 330 $aThe book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation. 410 0$aPoliMI SpringerBriefs,$x2282-2585 606 $aNeural networks (Computer science) 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aSystem theory 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aComplex Systems 615 0$aNeural networks (Computer science) 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aSystem theory. 615 14$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aComplex Systems. 676 $a003.857015118 676 $a003.857015118 700 $aSangiorgio$b Matteo$01198545 702 $aDercole$b Fabio 702 $aGuariso$b Giorgio 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910544860003321 996 $aDeep learning in multi-step prediction of chaotic dynamics$92919038 997 $aUNINA