Learning system dynamics via deep recurrent and conditional neural systems
dc.contributor.author | Pekmezci, Mehmet | |
dc.contributor.author | Uğur, E. | |
dc.contributor.author | Öztop, Erhan | |
dc.date.accessioned | 2023-01-06T13:34:47Z | |
dc.date.available | 2023-01-06T13:34:47Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-166543649-6 | |
dc.identifier.uri | http://hdl.handle.net/10679/8009 | |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9478006 | |
dc.description.abstract | Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the improvements in artificial neural networks. In this study, both LSTM-based recurrent deep learning method and CNMP-based conditional deep learning method were used to learn the system dynamics of the selected system using time series data. The effects of the amount of time series data needed for training and the initial input length needed for predictions made using the learned system model on both methods were analyzed. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2021 29th Signal Processing and Communications Applications Conference (SIU) | |
dc.rights | restrictedAccess | |
dc.title | Learning system dynamics via deep recurrent and conditional neural systems | en_US |
dc.type | Conference paper | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan | |
dc.contributor.ozuauthor | Öztop, Erhan | |
dc.identifier.wos | WOS:000808100700247 | |
dc.identifier.doi | 10.1109/SIU53274.2021.9478006 | en_US |
dc.subject.keywords | CNMP | en_US |
dc.subject.keywords | Deep learning | en_US |
dc.subject.keywords | LSTM | en_US |
dc.subject.keywords | System dynamics | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85111456985 | |
dc.contributor.ozugradstudent | Pekmezci, Mehmet | |
dc.relation.publicationcategory | Conference Paper - International - Institutional Academic Staff and PhD Student |
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