摘要
针对管道材料在土壤环境中的腐蚀速率预测问题,构建了一种基于过程神经元网络的动态预测模型。模型较好地模拟了金属在土壤环境中的腐蚀过程。文中给出了过程神经元网络预测模型和具体实现算法,对Q235钢管道材料在土壤中的腐蚀速率进行预测,结果表明:采用该方法得到的预测数据与实测值非常接近,验证了模型和算法的有效性。
To the corrosion rate prediction question of pipe materials in soil environment, a dynamic prediction model based on process neural networks is proposed in this paper. The model simulates the corrosion process of metal in the soil environment well. This paper introduces the prediction model based on process neural networks and the specific algorithm. We has predicted the corrosion rate to Q235 pipeline material in the soil environment already. The result shows that predictive data gained from the method are very close to measured data. It proves the effectiveness of model and algorithm.
出处
《齐齐哈尔大学学报（自然科学版）》
2008年第4期5-9,共5页
Journal of Qiqihar University(Natural Science Edition)
关键词
动态预测
过程神经元网络
模型
腐蚀速率
dynamic prediction
process neural networks
model
corrosion rate
