Pregled bibliografske jedinice broj: 499479
Targeting and synchronization at tokamak with recurrent artificial neural networks
Targeting and synchronization at tokamak with recurrent artificial neural networks // Neural computing & applications, 21 (2012), 5; 1065-1069 doi:10.1007/s00521-011-0527-4 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 499479 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Targeting and synchronization at tokamak with recurrent artificial neural networks
Autori
Rastović, Danilo
Izvornik
Neural computing & applications (0941-0643) 21
(2012), 5;
1065-1069
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
tokamak; synchronization; recurrent artificial neural networks
Sažetak
In this letter, we propose an adaptive recurrent artificial neural networks synchronization of H-mode and Edge Localized Modes that is important for obtaining a long pulse tokamak without disruption regime. The deterministic part of the plasma behavior should be synchronized with stochastic part by introducing stochastic artificial neural network.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo
POVEZANOST RADA
Projekti:
120-1201842-3048 - Umjetna inteligencija u upravljanju složenim nelinearnim dinamičkim sustavima (Kasać, Josip) ( CroRIS)
Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb,
Tehničko veleučilište u Zagrebu
Profili:
Danilo Rastović
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
