LSSVM在埋地管道腐蚀预测中的应用
Least squares support vector machine model applied in the prediction of buried pipeline corrosion rate
为提高埋地管道腐蚀速率预测的准确性,本文提出基于自适应粒子群优化(APSO)的最小二乘支持向量机(LSSVM)埋地管道腐蚀速率预测模型。LSSVM模型具有更快的学习速度,用APSO对LSSVM模型参数进行优化以期提高模型的预测精度。模拟结果表明，该方法应用于埋地管道腐蚀速率具有更高的预测精度。
o improve the predictive accuracy of buried pipeline corrosion rate, an adaptive particle swarm optimization (APSO) and least squares support vector machine (LSSVM) model is proposed to predict the buried pipeline corrosion rate. The LSSVM has a faster learning speed, and the APSO is used to optimize the LSSVM model parameter to improve the model prediction accuracy. The simulated results show that LSSVM method which is applied to the buried pipeline corrosion rate prediction has higher prediction accuracy.
陈翀、赵超
环境污染、环境污染防治自动化技术、自动化技术设备计算技术、计算机技术
埋地管道腐蚀速率预测最小二乘支持向量机
buried pipelinescorrosion rateforecastingleast squares support vector machine
陈翀,赵超.LSSVM在埋地管道腐蚀预测中的应用[EB/OL].(2017-05-04)[2025-08-21].http://www.paper.edu.cn/releasepaper/content/201705-250.点此复制
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