摘要
在利用随机森林算法(RF)进行路面附着系数估计时,存在模型构建过程中特征选择不够优化以及决策树集成的多样性不足等问题。为此,提出一种基于粒子群优化算法(PSO)对RF进行改进的方法,并给出算法流程。建立路面附着系数估计RF模型,使用PSO算法用于优化RF的参数配置,包括每棵树的特征数量、树的数量等关键因素,以增强模型的多样性和泛化能力。最后,在MATLAB/Simulink平台上搭建了联合仿真模型进行试验,对比试验结果表明:基于PSO-RF的随机森林路面附着系数估计方法能够克服传统RF方法中存在的局限性,其估计精度和稳定性均得到显著提升。
When using the Random Forest(RF)algorithm to estimate the road adhesion coefficient,there are issues such as insufficient optimization of feature selection during model construction and insufficient diversity in the ensemble of decision trees.To address this issue,a method based on Particle Swarm Optimization(PSO)algorithm to improve RF is proposed,and the algorithmic process is presented.An RF model for estimating the road adhesion coefficient is established,and the PSO algorithm is used to optimize the parameter configuration of RF,including key factors such as the number of features of each tree and the number of trees,so as to enhance the diversity and generalization capabilities of the model.At last,a joint simulation model is built on the MATLAB/Simulink platform for experiments.The comparative experimental results show that the random forest road adhesion coefficient estimation method based on PSO-RF can overcome the limitations of the traditional RF methods,and both the estimation accuracy and stability have been significantly improved.
作者
黄逊
查云飞
Huang Xun;Zha Yunfei(Fujian University of Technology,Fuzhou 350118)
出处
《汽车文摘》
2025年第4期42-47,共6页
Automotive Digest
关键词
路面附着系数
随机森林
粒子群优化
状态估计
Road adhesion coefficient
Random Forest(RF)
Particle swarm optimization(PSO)
State estimation
