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《Ordnance Material Science and Engineering》 2009-01
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Corrosion prediction of RE-Ni-Cu alloy cast iron based on neural network

WU Rigen1,DONG Junhui2,WANG Yurong1(1.Material Engineering Department,Baotou Vocational Technical College,Baotou 014030,China;2.College of Materials Science and Engineering,Inner Mongolia University of Technology,Huhhot 100062,China)  
The corrosion rate of Re-Ni-Cu alloy cast iron in high temperature concentrated alkaline solution were measured by the dynamic condition hydrometer method, and it was the sample data used for training and certification of BP neural network.The 4×15×1 and 4×15×8×1 BP neural network models were established by the toolbox function of MATLAB, and two network models were comparatively researched.The results show that under this sample set and training condition, prediction precision of four layer BP neural network is obviously higher than that of three layers BP neural network, and four layers BP neural network canbe used for corrosion prediction of RE-Ni-Cu alloy cast iron in high temperature concentrated alkaline solution.
【CateGory Index】: TG143.9
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