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《Ordnance Material Science and Engineering》 2012-01
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Application of artificial neural network in corrosion depth prediction of alloy cast iron

WANG Yurong,WU Rigen(Humanities and Artist Design Department,Baotou Vocational Technical College,Baotou 014035,China)  
The sample data of BP neural network were measured by the dynamic and static condition hydrometer method.The BP neural network model for dynamic and static depth prediction was established by the toolbox function of MATLAB,and the prediction error of two corrosion test methods is comparatively studied.The results show that 5×8×10×1BP neural network can be used for dynamic and static corrosion depth prediction of alloy cast iron in caustic soda solution,and the more accurate the sample data of corrosion test,the smaller the prediction error of 5×8×10×1BP neural network.
【CateGory Index】: TG143.9
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