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BP神经网络在铜及铜合金海水腐蚀预测中的应用
引用本文:邓春龙,李文军,孙明先,郭为民,刘 伟.BP神经网络在铜及铜合金海水腐蚀预测中的应用[J].海洋科学,2006,30(3):16-20.
作者姓名:邓春龙  李文军  孙明先  郭为民  刘 伟
作者单位:洛阳船舶材料研究所青岛分部,海洋腐蚀与防护国防科技重点实验室,山东,青岛,266071
摘    要:根据实海环境数据及材料腐蚀数据,利用BP结构神经网络建立了铜及铜合金在实海环境中腐蚀速度与环境因素、材料成分之间神经网络预测模型。利用建立的预测模型分析了环境因素对铜及铜合金的腐蚀速度的影响。分析结果表明,温度的升高及生物污损促进铜及铜合金的腐蚀,而pH、盐度和氧浓度的升高对浸泡1年的材料腐蚀速度有明显的抑制作用。

关 键 词:人工神经网络  腐蚀  预测
文章编号:1000-3096(2006)03-0016-05
收稿时间:2005-03-21
修稿时间:2005-03-212005-05-09

BP Neural network approach in the prediction of copper and copper alloy corrosion in seawater
DENG Chun-long,LI Wen-jun,SUN Ming-xian,GUO Wei-min,LIU Wei.BP Neural network approach in the prediction of copper and copper alloy corrosion in seawater[J].Marine Sciences,2006,30(3):16-20.
Authors:DENG Chun-long  LI Wen-jun  SUN Ming-xian  GUO Wei-min  LIU Wei
Institution:Luoyang Ship Material Research Institute Qingdao branch, Marine Corrosion and Protection State Key Laboratory, Qingdao 266071, China
Abstract:Based on the environment data and material corrosion data,using BP artificial neural network method,models for prediction of corrosion rates of copper and copper alloy were proposed to describe the relationship between corrosion rate,environment factors,and component of material.Use the model to analyse the affect of environment factors on corrosion rates of copper and copper alloy.The result of analyses is that higher temperature and bio-fouling accelerate copper and copper alloy corrosion,but the pH,oxygen solubility and salinity decelerate corrosion clearly only in one-year test.
Keywords:artificial neural network  corrosion  prediction
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