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Sensors 2016, 16(3), 351; doi:10.3390/s16030351

Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests

1
Instrument and Meter Engineering, Southeast University, Nanjing 210096, China
2
The 14th Research Institute, China Electronics Technology Group Corporation, Nanjing 210013, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 30 November 2015 / Revised: 28 February 2016 / Accepted: 1 March 2016 / Published: 10 March 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)

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. View Full-Text
Keywords: relevance vector machine; multiple kernels; particle swarm optimization; drawbar pull; real vehicle test relevance vector machine; multiple kernels; particle swarm optimization; drawbar pull; real vehicle test
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, F.; Sun, W.; Lin, G.; Zhang, W. Prediction of Military Vehicle’s Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests. Sensors 2016, 16, 351.

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