支持向量机与神经网络在减振器建模中的应用  

Applications of Support Vector Regression and Neural Network in Modelling the Hydraulic Damper

作　　者：王先云 郭孔辉 

机构地区：[1]汽车仿真与控制国家重点实验室,长春130025

出　　处：《科学技术与工程》2011年第33期8219-8224,共6页Science Technology and Engineering

基　　金：中国高水平汽车自主创新能力建设项目(200822010001531)资助

摘　　要：提出了利用支持向量机回归建立减振器非参数模型的方法。之后,利用支持向量机建立的模型与两类神经网络模型进行了对比。一类是反向传播神经网络,另一类是径向基函数神经网络。这三种模型分别在虚拟减振器与真实减振器上进行了比较。比较结果证明反向传播神经网络对虚拟减振器的辨识结果最好,而支持向量机回归算法对真实减振器的辨识效果最好。其原因是由于真实减振器的试验数据均具有噪声,而支持向量机对噪声具有一定的鲁棒性。Nonparametric models of hydraulic damper based on support vector regression（SVR） are developed.Then these models are compared with two kinds neural network models.One is backpropagation neural network（BPNN） model;another is radial basis function neural network（RBFNN） model.Comparisons are carried out both on virtual damper and actual damper.The force-velocity relation of a virtual damper is obtained based on a rheological model.Then these data are used to identify the characteristics of the virtual damper.The dynamometer measurements of an actual displacement-dependent damper are obtained by experiment.And these data are used to identify the characteristics of this actual damper.The comparisons show that BPNN model is best at identifying the characteristics of the virtual damper,but SVR model is best at identifying the characteristics of the actual damper.The reason is that all experimental data include noise more or less.When the amplitude of the noise is smaller than the parameter ε of SVR,the noise can not affect the construction of the resulting model.So when training a model based on the experimental data,SVR is superior to other neural networks methods.

关 键 词：车辆模拟 支持向量机回归 神经网络 减振器 

分 类 号：U463.33[机械工程—车辆工程]