NDLI: A neural network approach for the automated detection of faulty electromagnetic probes in a nuclear fusion experiment

Content Provider	IEEE Xplore Digital Library
Author	Dona, A. Manduchi, G. Moro, M.
Copyright Year	1994
Description	Author affiliation: Istituto Gas Ionizzati, CNR, Padova, Italy (Dona, A.; Manduchi, G.)
Abstract	RFX (Reverse Field Experiment) is one of the large nuclear fusion experiments within the framework of the co-ordinated nuclear fusion research program of the European Community. Its configuration requires precise knowledge of the magnetic quantities for the understanding of the plasma behaviour. Due to the large number of signals acquired from the electromagnetic probes, an automated test procedure is required to monitor their functionality. We report the results of a novel approach for the automatic detection of faulty signals, based on Neural Network techniques. The Adaptive Resonance Theory (ART) network architecture proved to be best suited for this kind of application.
Starting Page	1281
Ending Page	1286
File Size	527015
Page Count	6
File Format	PDF
ISBN	0780313283
DOI	10.1109/IECON.1994.397978
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-09-05
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Fault detection Fusion reactors Plasmas Probes Automatic testing Computerized monitoring Signal detection Resonance Subspace constraints
Content Type	Text
Resource Type	Article

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A neural network approach for the automated detection of faulty electromagnetic probes in a nuclear fusion experiment