融合支持向量机和特征降维方法的人-椅系统振动模型研究

Modeling of the dynamic seat-occupant system with the integration of support vector machine and the feature dimensionality reduction methods

人-椅系统的振动传递特性受人体体征参数、座椅结构、乘坐环境等多种复杂因素影响。在人体振动实验研究的基础上,寻求构建一种基于支持向量机回归的座椅频响函数预测模型,分别采用递归特征消除法和主成分分析法对人体体征参数进行降维,并将低维特征输入预测模型,以实现对人-椅系统频响函数及其正交轴效应的预测。结果显示,相比传统支持向量机回归模型,应用主成分分析法降低体征参数关联,可以显著降低模型预测误差,预测值与实测值拟合度可达92%。通过递归特征消除法剔除次要体征参数,可进一步提升预测精度,预测值与实测值拟合度达94%。研究表明,基于特征降维优化的支持向量机回归预测模型能够有效筛选人体振动模型中输入参数的冗余信息,并提升座椅频响函数的计算效率和预测精度。

The dynamic response of the seat-occupant system to vibration exhibits complex dependencies on various factors,including anthropometric parameters,the seat structure,and riding environments,etc.Drawing from an experimental investigation into whole body vibration,this study aims to develop a predictive model for seat transmissibility using support vector machine regression.The recursive feature elimination method and principal component analysis are employed to reduce the dimensionality of anthropometric parameters.The resultant low-dimensional features are then integrated into the model to predict the seat transmissibility and cross-axis effect.The results show that compared with the traditional support vector machine regression model,the application of principal component analysis to reduce the correlation of anthropometric parameters can significantly reduce the prediction error of the model,and the fitting degree between the predicted value and the measured value can reach 92%.The accuracy of prediction can be further improved by removing the minor anthropometric parameters through the recursive feature elimination method,and the fitting degree between the predicted value and the measured value is 94%.The findings demonstrate that the support vector machine regression model,based on the feature dimensionality reduction optimization,can effectively sieves out the redundant information of input parameters in the model.This refinement enhances both the efficiency and accuracy of predicting the seat transmissibility.