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- W2134420258 abstract "Preliminary results are presented for artificial neural network (ANN) models of the available output noise temperature of a FET-based synthetic cold load. Two different ANNs were studied for this application: the radial basis function (RBF) and the Levenberg-Marquardt (LM) backpropagation (BP). The best average relative error (ARE) and maximum local relative error (MLRE) results for the model of incident noise temperature versus load impedance were 0.1439% and 1.1544% respectively. The best ARE and MLRE results for the model of incident noise temperature versus load reflection coefficient were 0.1810% and 1.5044% respectively." @default.
- W2134420258 created "2016-06-24" @default.
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- W2134420258 date "2007-06-01" @default.
- W2134420258 modified "2023-09-27" @default.
- W2134420258 title "ANN modeling of synthetic cold loads" @default.
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- W2134420258 doi "https://doi.org/10.1109/arftg.2007.5456338" @default.
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