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- W2007464729 abstract "World-wide data for the atmospheric corrosion of steel and zinc were used to train and test neural networks. The cross validity technique was employed as the criterion to stop training. The statistical performance of the neural network was expressed as an average of five training and testing results. Multiple correlation coefficients showed that the neural network could account for about 70% of the variance in the corrosion data for steel and zinc. The testing results showed that predictions of corrosion data not included in the original training were close to practical data. Sensitivity analysis also demonstrated the effects of sulphur dioxide, chloride and exposure time on atmospheric corrosion in specific environments." @default.
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- W2007464729 date "1999-10-01" @default.
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- W2007464729 title "Phenomenological modelling of atmospheric corrosion using an artificial neural network" @default.
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- W2007464729 doi "https://doi.org/10.1016/s0010-938x(99)00024-4" @default.
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