Szczegóły publikacji
Opis bibliograficzny
Modelling hysteresis with neural network / Waldemar RĄCZKA, Marek SIBIELAK, Jarosław KONIECZNY // W: ICCC'2023 [Dokument elektroniczny] : 24th International Carpathian Control Conference : 12–14 June 2023, Szilvásvárad, Hungary : proceedings / eds. Dániel Drótos, [et al.]. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2023. — Dod. ISBN: 979-8-3503-1023-8, 979-8-3503-1021-4. — e-ISBN: 979-8-3503-1022-1. — S. 394–399. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 399, Abstr. — Publikacja dostępna online od: 2023-07-19
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149314 |
|---|---|
| Data dodania do BaDAP | 2023-11-22 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCC57093.2023.10178984 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
An example of a system with hysteresis is an actuator made of SMA. In the paper, the LSTM neural network was used to model the actuator. The focus was pointed to its hysteretic part. Neural network structure and learning method are briefly presented. Data used for training was obtained from the Preisach hysteresis model. This model was used to generate the datasets used for training and testing. Thanks to this approach, we can generate data quickly and in a fully controllable way. Thanks to this, experiments can be thoroughly planned and fully repeatable. The advantage and disadvantage at the same time is the lack of disturbances. The main goal of the paper was to model hysteresis on an example of an SMA actuator, but in fact, it was interesting if a neural network could describe hysteresis loop because literature research shows that usually neural networks are used to model hysteresis with a hysteretic element in combination modelled in another way.
