title: Prediction of Military Vehicle's Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests.
abstract: The scientific and effective prediction of drawbar pull is of great importance in the evaluation of military vehicle trafficability. Nevertheless, the existing prediction models have demonstrated lots of inherent limitations. In this framework, a multiple-kernel relevance vector machine model (MkRVM) including Gaussian kernel and polynomial kernel is proposed to predict drawbar pull. Nonlinear decreasing inertia weight particle swarm optimization (NDIWPSO) is employed for parameter optimization. As the relations between drawbar pull and its influencing factors have not been tested on real vehicles, a series of experimental analyses based on real vehicle test data are done to confirm the effective influencing factors. A dynamic testing system is applied to conduct field tests and gain required test data. Gaussian kernel RVM, polynomial kernel RVM, support vector machine (SVM) and generalized regression neural network (GRNN) are also used to compare with the MkRVM model. The results indicate that the MkRVM model is a preferable model in this case. Finally, the proposed novel model is compared to the traditional prediction model of drawbar pull. The results show that the MkRVM model significantly improves the prediction accuracy. A great potential of improved RVM is indicated in further research of wheel-soil interactions.
authors.affiliation: Instrument and Meter Engineering, Southeast University, Nanjing 210096, China. 230109465@seu.edu.cn.|The 14th Research Institute, China Electronics Technology Group Corporation, Nanjing 210013, China. 230109465@seu.edu.cn.
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
authors.affiliation: The 14th Research Institute, China Electronics Technology Group Corporation, Nanjing 210013, China. sunwei0011@sina.com.
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.affiliation: Instrument and Meter Engineering, Southeast University, Nanjing 210096, China. andrew.lin@seu.edu.cn.
authors.forename: Guoyu
authors.initials: G
authors.lastname: Lin
authors.affiliation: Instrument and Meter Engineering, Southeast University, Nanjing 210096, China. zhangwg@seu.edu.cn.
authors.forename: Weigong
authors.initials: W
authors.lastname: Zhang
keywords: drawbar pull; multiple kernels; particle swarm optimization; real vehicle test; relevance vector machine