微型电脑应用2025，Vol.41Issue(2)：265-268,272,5.
基于ALPO的碳钢大气腐蚀速率预测研究
Research on Prediction of Atmospheric Corrosion Rate of Carbon Steel Based on ALPO
杨彪 1闫莹 2刘振栋3
作者信息
- 1. 上海电子信息职业技术学院,申安网络安全产业学院,上海 201411
- 2. 华东理工大学,资源与环境工程学院,上海 200237
- 3. 上海第二工业大学,计算机与信息工程学院,上海 201209
- 折叠
摘要
Abstract
An adaptive least squares support vector machine particle optimization(ALPO)model that integrates adaptive muta-tion particle swarm optimization(AMPSO)and least squares support vector machine(LSSVM)is proposed in this paper to ad-dress the issue of predicting carbon steel corrosion rates in atmospheric environments.AMPSOis utilized to optimize the core parameters of LSSVM,overcome the randomness of parameter selection,and improve the prediction accuracy of carbon steel corrosion rates.The predictive performance of the ALPOmodel and traditional models is evaluated based on the iberoamerican corrosion map project(MICAT)carbon steel corrosion rate dataset.The results show that ALPOreduces the mean absolute error(MAE)mean absolute percentage error(MAPE)and root mean square error(RMSE)by 40.04 percentage points,38.01 percentage points and 42.83 percentage points,respectively,compared to the LSSVM model.This indicates that the ALPO model provides more accurate and feasible predictions for atmospheric corrosion rates of carbon steel,and offers a new method for constructing prediction model for carbon steel atmospheric corrosion rates.关键词
碳钢/大气腐蚀/粒子群优化/支持向量回归/腐蚀速率预测Key words
carbon steel/atmospheric corrosion/particle swarm optimization/support vector machine/corrosion rate prediction分类
信息技术与安全科学引用本文复制引用
杨彪,闫莹,刘振栋..基于ALPO的碳钢大气腐蚀速率预测研究[J].微型电脑应用,2025,41(2):265-268,272,5.
