摘要
利用人工神经网络从已有的土壤腐蚀试验数据中通过训练求得土壤的理化性能与碳钢在土壤中的腐蚀速度之间的非线性关系，从而预测碳钢在土壤中的腐蚀速度采用的神经网络结构为５—８—１的形式，学习算法采用ＢＰ算法．结果表明，含水量和Ｃｌ－离子是影响碳钢土壤腐蚀的主要因素．
This paper introduce neural network approach to study the non-linear reationshipbetween the physical and chemical properties and corrosion rate of carbon steel in soil from the collected corrosion data. The corrosion rate of carbon steel in soil can be predicted by trained neural nerwork. In the study, a neural network with 5-8-1 structure was used. The learning algorithm is BP (back-propagation) algorithm. As a result, H2O and Cl-ion in soil are dominant factors influencing corrosion of carbon steel.
出处
《腐蚀科学与防护技术》
CAS
CSCD
1995年第3期258-262,共5页
Corrosion Science and Protection Technology
基金
国家自然科学基金
