Coupling of recurrent and static neural network approaches for improved multi-step ahead time series prediction

Winter, M., Breitsamter, C.

doi:https://doi.org/10.1007/978-3-319-64519-3

Title:: Coupling of recurrent and static neural network approaches for improved multi-step ahead time series prediction
Document type:: Konferenzbeitrag
Contribution type:: Textbeitrag / Aufsatz
Author(s):: Winter, M., Breitsamter, C.
Pages contribution:: 433-442
Book / Congress title:: 20th STAB/DGLR Symposium on New Results in Numerical and Experimental Fluid Mechanics, 2016
Congress (additional information):: Braunschweig; Germany; 8 November 2016 through 9 November 2016; Code 203289
Volume:: 136
Year:: 2018
Covered by:: Scopus
Print-ISBN:: 978-3-319-64518-6
E-ISBN:: 978-3-319-64519-3
Bookseries title:: Notes on Numerical Fluid Mechanics and Multidisciplinary Design
Bookseries ISSN:: 1612-2909
Language:: en
Fulltext / DOI:: doi:https://doi.org/10.1007/978-3-319-64519-3
WWW:: https://link.springer.com/search?facet-eisbn=978-3-319-64519-3&facet-content-type=Chapter&query=Winter
TUM Institution:: Lehrstuhl für Aerodynamik
BibTeX