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- W2037687429 abstract "The accuracies and sensitivities of several parameter estimation paradigms are compared for electromagnetic ellipticity data. Images of the ellipticity are created for a cylindrical, conductive target located at various depths and offsets from a transmitter. The ellipticity patterns are variously represented by the peak amplitude and location, the peak and adjacent low values, the subsampledimageandtheFourier transform components. Backpropagation, Extended Delta Bar Delta, Directed Random Search, Functional Link and Self Organizing Map to Backpropagation paradigms were trained and tested. The Directed Random Search technique had the lowest average errors for all test data representations. This paradigm also proved relatively insensitive to small fluctuations in the input parameters. While the Directed Random Search is easy to train, for large networks the training times are longer than the other paradigms." @default.
- W2037687429 created "2016-06-24" @default.
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- W2037687429 date "1991-01-01" @default.
- W2037687429 modified "2023-09-27" @default.
- W2037687429 title "Comparison of accuracy and sensitivity of neural network parameter‐estimation paradigms for EM subsurface images" @default.
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- W2037687429 doi "https://doi.org/10.1190/1.1888939" @default.
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