青岛大学学报（自然科学版）2026，Vol.39Issue(2)：48-54,7.DOI:10.3969/j.issn.1006-1037.2026.02.07
基于GTO-SVM的轮轨接触预测模型
Prediction Model for Wheel-rail Contact Based on GTO-SVM
王昊 1徐同庆 1武佳宝 1焦贤运 1张骞1
作者信息
- 1. 青岛大学机电工程学院,青岛 266071
- 折叠
摘要
Abstract
To address the issues of hyperparameter sensitivity and insufficient generaliza-tion ability of the traditional Support Vector Machine(SVM)model in wheel-rail contact state prediction,the Gorilla Troops Optimizer(GTO)algorithm was utilized to globally optimize key hyperparameters of the SVM model,such as the penalty coefficient and ker-nel function,and the GTO-SVM wheel-rail contact state prediction model.was estab-lished.A complete prediction framework encompassing data preprocessing,feature engi-neering,and model validation was constructed,measured multi-source data(encompass-ing wheel-rail contact force,contact position,and wear extent)was acquired from a spe-cific high-speed railway line for the purpose of building an experimental dataset.Simula-tion results show that compared with the standard SVM model,the GTO-SVM model a-chieves a reduced mean absolute error(MAE)in contact force prediction,with the coeffi-cient of determination(R2)improved to 0.996 21.In complex working condition tests,RMSE is lower than that of the traditional model,verifying the strong generalization abili-ty of the GTO-SVM model under complex operating conditions.关键词
支持向量机/大猩猩种群优化算法/重载铁路/模型预测Key words
support vector machine/gorilla troops optimizer/heavy-haul railway/model prediction分类
信息技术与安全科学引用本文复制引用
王昊,徐同庆,武佳宝,焦贤运,张骞..基于GTO-SVM的轮轨接触预测模型[J].青岛大学学报（自然科学版）,2026,39(2):48-54,7.
