集美大学学报（自然科学版）2026，Vol.31Issue(3)：308-320,13.DOI:10.19715/j.jmuzr.2026.03.05
基于PCA-SVR的集装箱码头设备配置
Container Terminal Equipment Configuration Based on PCA-SVR
摘要
Abstract
This paper proposes an optimized model(PCA-SVR)that combines support vector regression(SVR)and principal component analysis(PCA)to optimize the equipment configuration of container termi-nals,aiming to improve the efficiency of terminal handling operations.By collecting equipment configuration da-ta from multiple container terminals,the SVR model is constructed and trained,optimizing model parameters through cross-validation and grid search techniques.The data indicate that the model using the radial basis function(RBF)kernel exhibited the best performance after optimization.To address the issue of feature corre-lation,PCA was introduced for dimensionality reduction,which effectively accelerated model training and en-hanced its robustness.Model results show that the PCA-SVR model has high accuracy in identifying redundan-cies or deficiencies in equipment configuration,demonstrating superior fitting performance.关键词
支持向量回归/主成分分析/集装箱码头/设备配置优化/交叉验证/网格搜索技术Key words
support vector regression/principal component analysis/container terminal/equipment configu-ration optimization/cross-validation/grid search technique分类
交通工程引用本文复制引用
詹世龙,曾艳,柯冉绚..基于PCA-SVR的集装箱码头设备配置[J].集美大学学报（自然科学版）,2026,31(3):308-320,13.基金项目
国家重点研发项目(2021YFB3901505) （2021YFB3901505）
