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中国腐蚀与防护学报  1996, Vol. 16 Issue (4): 307-310    
  研究报告 本期目录 | 过刊浏览 |
基于人工神经网络的金属土壤腐蚀预测方法
郭稚弧;邢政良;金名惠;孟厦兰
华中理工大学化学系
PREDICTING CORROSION RATE OF MILD STEEL IN SOIL BASED ON ARTIFICIAL NEURAL NETWORK
GUO Zhihu; XING Zhengliang; JIN Minghui; MENG Xialan (Huazhong University of Science and Technology; Wuhan 430074)
全文: PDF(264 KB)  
摘要: 将神经网络用于金属土壤腐蚀研究,利用神经网络的学习特征和高度的非线性特征,以土壤理化性能、腐蚀时间、A3钢在土壤腐蚀试验1、2、8个月的腐蚀数据作为网络训练样本,对土壤中埋片24个月的A3钢腐蚀速率进行预测,并对结果进行了分析。
关键词 神经网络权值学习样本土壤腐蚀    
Abstract:Artificial neural network, possessing learning and non-linear character, was applied to study corrosion of mild steel in soil. A neural network typically consists of many simple neurons like processing elements called "cell" or "nodes" that interact with other cells using numerical weighted connection. In this study,a neural network with 6-10-1 structure, namely 6 input nodes, 10 hidden layer nodes, 1 output node, was used. The learning algorithm was BP (Back-Propagation) algorithm. Corrosion tests of mild steel in soil were carried out with orthogonal test method, in which five corrosion factors, namely pH value,Cl-, H2O,SO_4~2- and Fe2+ content, were considered and test data of sixteen groups were obtained. These data were used as sample set to train neural network. The inputs of neural network were the five corrosion factors and test duration, the output of neural network was corrosion rate of steel in soil.The research results showed that soil corrosion rates of mild steel in 24 months could be predicted by the trained artificial neural network, and were basically in agreement with experimental data.
Key words Neural network    Weight    Learning sample    Soil corrosion
收稿日期: 1996-08-25     

引用本文:

郭稚弧;邢政良;金名惠;孟厦兰. 基于人工神经网络的金属土壤腐蚀预测方法[J]. 中国腐蚀与防护学报, 1996, 16(4): 307-310.
. PREDICTING CORROSION RATE OF MILD STEEL IN SOIL BASED ON ARTIFICIAL NEURAL NETWORK. J Chin Soc Corr Pro, 1996, 16(4): 307-310.

链接本文:

https://www.jcscp.org/CN/      或      https://www.jcscp.org/CN/Y1996/V16/I4/307

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