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

摘要：

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 forcevelocity 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 ? 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： vehicle simulation support vector regression neural network hydraulic damper

作者: Konghui Guo Xianyun Wang

作者单位: State Key Laboratory of Automobile Dynamic Simulation Jilin University Changchun, China

会议类型: 国际会议

会议名称: 2010 6th International Conference on MENS NANO,and Smart System(2010年微机电纳米、智能系统国际会议 ICMENS 2010)

会议地点: 长沙

会议语种:英文

页码: 160-164

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）