机器学习在预测越野环境中的路径深度方面的应用
Behzad Golanbari1, Aref Mardani2, Nashmil Farhadi1
1Department of Mechanical Engineering of Biosystems, Urmia University, Urmia, Iran.
Scientific reports
|February 14, 2025
概括
从越野车辆预测路径深度对于性能和土壤健康至关重要. 一种混合的秘书鸟优化算法-分类提升 (SBOA-CatBoost) 模型在估计深度方面取得了卓越的准确性.
科学领域:
- 农业工程 农业工程
- 土壤力学 土壤力学
- 机器学习应用 机器学习应用
背景情况:
- 越野车辆的泥深度对车辆的性能和土壤紧缩有重大影响.
- 传统的方法与影响深度的复杂,非线性关系作斗争,导致估计错误.
研究的目的:
- 使用机器学习准确预测场外车辆造成的路径深度.
- 通过将优化算法与机器学习模型集成来提高预测准确性.
主要方法:
- 使用了分类提升 (CatBoost) 算法来预测路径深度.
- 集成的灰狼优化 (GWO),粒子群优化 (PSO) 和秘书鸟优化算法 (SBOA) 用于超参数调整.
- 在受控条件下收集了270个实验样本,垂直负载,速度,引装置和通过次数各不相同.
主要成果:
- 这款SBOA-CatBoost混合动力车型表现出卓越的性能.
- 实现了0.35mm的根平均平方误差 (RMSE) 和0.97707.2的确定系数 (R2).
- 平均绝对百分比误差 (MAPE) 为1.2%,表明预测准确度很高.
结论:
- 该SBOA-CatBoost模型提供了一个非常准确的方法来预测越野车辆的路径深度.
- 这种方法可以帮助提高车辆的性能和减轻土壤紧缩.
- 优化的机器学习模型比传统的估计技术提供了显著的优势.
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