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- W2765165720 abstract "Committees of multilayer neural networks were used to estimate the appropriate surface area and thickness of RF absorbing material needed to achieve a desired quality factor (Q) inside a reverberation chamber. The networks were trained with Bayesian Regularization to avoid overfitting. Monte Carlo cross-validation was used to develop confidence bounds on the accuracy of the network committees." @default.
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- W2765165720 date "2017-08-01" @default.
- W2765165720 modified "2023-10-18" @default.
- W2765165720 title "Estimation of required absorbing material dimensions inside metal cavities using neural networks" @default.
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- W2765165720 doi "https://doi.org/10.1109/isemc.2017.8077843" @default.
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