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- W2279706247 abstract "This paper proposes the use of Fuzzy Neural Network (FNN) approaches for the early detection of disruption in tokamak plasmas. The fuzzy neural models is able to combine signals from various different plasma diagnostics in order to make an estimation of the expected time of disruption. This is, in turn, useful for having sufficient margin to initiate a disruption avoidance action. The inclusion of many diagnostic measurements results in a much more accurate prediction than that provided by traditional physical approaches. The use of fuzzy logic concept is suggested by the consideration that those previous techniques make use of expert knowledge for deciding about the onset of a disruption. In addition, learning approaches allow to tune the model. The proposed method appears to be a step forward with respect to more conventional NN approach." @default.
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- W2279706247 date "1999-01-01" @default.
- W2279706247 modified "2023-10-16" @default.
- W2279706247 title "Real Time Neural Network Disruption Prediction in Tokamak Reactors" @default.
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- W2279706247 doi "https://doi.org/10.1007/978-1-4471-0877-1_39" @default.
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