Full metadata record
| DC pole | Hodnota | Jazyk |
|---|---|---|
| dc.contributor.author | Karban, Pavel | |
| dc.contributor.author | Petrášová, Iveta | |
| dc.contributor.author | Doležel, Ivo | |
| dc.date.accessioned | 2023-03-06T11:00:30Z | - |
| dc.date.available | 2023-03-06T11:00:30Z | - |
| dc.date.issued | 2022 | |
| dc.identifier.citation | KARBAN, P. PETRÁŠOVÁ, I. DOLEŽEL, I. The principle of prediction of complex time-dependent nonlinear problems using RNN. In Proceedings of the 23rd International Conference on Computational Problems of Electrical Engineering, CPEE 2022. Piscataway: IEEE, 2022. s. nestránkováno. ISBN: 979-8-3503-9625-6 | cs |
| dc.identifier.isbn | 979-8-3503-9625-6 | |
| dc.identifier.uri | 2-s2.0-85142158137 | |
| dc.identifier.uri | http://hdl.handle.net/11025/51682 | |
| dc.format | 4 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | Proceedings of the 23rd International Conference on Computational Problems of Electrical Engineering, CPEE 2022 | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům | cs |
| dc.rights | © IEEE | en |
| dc.title | The principle of prediction of complex time-dependent nonlinear problems using RNN | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | An approach based on recurrent neural networks (RNNs) is applied to verify the possibility of using surrogate models for the prediction of dynamic nonlinear problems. Modeling complex time dependencies is currently still a challenge, when the structure of the neural network needs to be adapted to the dynamics of the problem. In this paper, the possibility of using prediction in space-time problems is illustrated by the possibility of using it to predict the course of the current in a simple RL circuit that is powered by a voltage source. | en |
| dc.subject.translated | surrogate model | en |
| dc.subject.translated | recurrent neural networks | en |
| dc.subject.translated | LSTM | en |
| dc.subject.translated | analytical model | en |
| dc.subject.translated | dynamic nonlinear problem | en |
| dc.identifier.doi | 10.1109/CPEE56060.2022.9919650 | |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43938733 | |
| dc.project.ID | SGS-2021-011/Rozvoj technik snižování řádu systému v elektrotechnických aplikacích | cs |
| Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference papers (RICE) Konferenční příspěvky / Conference Papers (KEP) OBD | |
Soubory připojené k záznamu:
| Soubor | Velikost | Formát | |
|---|---|---|---|
| The_principle_of_prediction_of_complex_time-dependent_nonlinear_problems_using_RNN.pdf | 689,72 kB | Adobe PDF | Zobrazit/otevřít Vyžádat kopii |
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