A neural network approach for the detection of the locking position in RFX (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• A neural network approach for the detection of the locking position in RFX (Articolo in rivista) (literal)

Anno

• 2001-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1016/S0920-3796(00)00551-2 (literal)

Alternative label

• O. Barana; G. Manduchi; A. Serri; P. Sonato (2001)
  A neural network approach for the detection of the locking position in RFX
  in Fusion engineering and design; ELSEVIER SCIENCE SA, PO BOX 564, 1001 LAUSANNE (Svizzera)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• O. Barana; G. Manduchi; A. Serri; P. Sonato (literal)

Pagina inizio

• 9 (literal)

Pagina fine

• 20 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• E-ISSN: 1873-7196 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.sciencedirect.com/science/article/pii/S0920379600005512 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 55 (literal)

Rivista

• Fusion engineering and design (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 12 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 1 (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1,2 : Consorzio RFX, Corso Stati Uniti 4, I-35127 Padova, Italy / 3,4 : Dipartimento di Ingegneria Elettrica ed Elettronica, Università di Cagliari, Piazza d'Armi, I-09123 Cagliari, Italy. (literal)

Titolo

• A neural network approach for the detection of the locking position in RFX (literal)

Abstract

• In the RFX (reversed field experiment), one of the most important reversed field pinch (RFP) devices in the fusion community, wall locked modes have always been present. Recently, a new technique has demonstrated the possibility of inducing a continuous rotation of the modes with respect to the wall. The non-linear coupling of the m = 0 and m = 1 modes has been used to decouple the modes themselves, and the mode rotation has been induced by means of a pre-programmed waveform of a toroidal magnetic field rotating ripple. Consequently, a feedback system for detecting the locked mode position along the toroidal co-ordinate and able to create a continuous rotation with variable speed has been envisaged. Neural networks (NNs) represent a promising approach for rapid detection of the locked mode angular position in such a system, and in this paper the performances of different NNs trained to identify the locked mode position are compared and discussed. In particular, their robustness to noise is analyzed, and it is shown that NNs provide reliable results, sometimes better than those computed with fourier analysis. (literal)

Editore

• ELSEVIER SCIENCE SA (Editore)

Prodotto di

• Esperimento RFX ed attività collegate (ET.P05.003.001) (Modulo)
• Institute of ionized gas (IGI) (Istituto)

Autore CNR

• GABRIELE MANDUCHI (Unità di personale interno)

Incoming links:

Autore CNR di

• GABRIELE MANDUCHI (Unità di personale interno)

Prodotto

• Institute of ionized gas (IGI) (Istituto)
• Esperimento RFX ed attività collegate (ET.P05.003.001) (Modulo)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Fusion engineering and design (Rivista)

Editore di

• ELSEVIER SCIENCE SA (Editore)