http://www.cnr.it/ontology/cnr/individuo/prodotto/ID26103
EM signal integrity via neural network analysis for the RFX-mod experiment (Articolo in rivista)
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- EM signal integrity via neural network analysis for the RFX-mod experiment (Articolo in rivista) (literal)
- Anno
- 2011-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.fusengdes.2011.03.010 (literal)
- Alternative label
Rita S. Delogu; David Terranova (2011)
EM signal integrity via neural network analysis for the RFX-mod experiment
in Fusion engineering and design; ELSEVIER SCIENCE SA, PO BOX 564, 1001 LAUSANNE (Svizzera)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Rita S. Delogu; David Terranova (literal)
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- La rivista è pubblicata anche online con ISSN 1873-7196 (Editore: Elsevier Science SA) (literal)
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- http://www.sciencedirect.com/science/article/pii/S0920379611002808 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- Scopu (literal)
- ISI Web of Science (WOS) (literal)
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- Consorzio RFX, Associazione Euratom-ENEA sulla Fusione, Corso Stati Uniti, 4, I-35127 Padova, Italy; Consorzio RFX, Associazione Euratom-ENEA sulla Fusione, Corso Stati Uniti, 4, I-35127 Padova, Italy (literal)
- Titolo
- EM signal integrity via neural network analysis for the RFX-mod experiment (literal)
- Abstract
- The RFX-mod electromagnetic measurement system is constituted of 744 independent probes whose signals are electronically conditioned by an integration/amplification section. During experimental sessions the probes integrity is controlled by a series of post-shot softwares which determine if a probe is still working or not and correct off-sets and drifts, but no method, apart from the visual inspection of a signal, is available to recognize if the corresponding channel in the integration/amplification section is about to break. In order to overcome this lack a neural network approach has been applied. The neural network implemented here is built performing a geometrical synthesis of a supervised Multi Layer Perceptron, then the trained net is used to predict a possible failure of the corresponding channel in the integration/amplification section. To perform the prediction the neural network is used as a non linear regressor, the synaptic weights of the trained net can be considered as a neural transform of the system, the variation of those weights in the test phase is symptom that the channel is not working properly. The procedure has been tested on a subset of electromagnetic signals and in this paper the results are presented. (literal)
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