Analysis of Tank Driving Simulation Training Results Based on Support Vector Machine

doi:10.3969/j.issn.1000-1093.2019.09.021

Abstract

Abstract: Training by tank driving simulator is an important way of improving equipment skill. A method of training effect analysis of tank driving simulator based on support vector machine (SVM) is proposed for the problems resulted from ignoring the collection and analysis of training data and the increase in training quality. In order to solve the issue of choosing SVM parameters,an adaptive particle swarm optimization (APSO) algorithm is adopted to determine the SVM parameters. Dynamic weight parameters are designed and the related inertance is entrusted for realizing the self-adaption of particle. The multi-location inquiry mechanism and the information from extreme point are used to keep the balance dot diversity of different particles.The SVM parameters can be automatically optimized by iterating and optimizing the object function. After the SVM based on particle swarm algorithm is used for the training effect analysis of tank driving simulator, the adverse effect of multidimensional factors on training score can be overcome. The experimental results show that SVM can be feasible and effective in the training effect analysis of tank driving simulator. Key

Key words: tank, supportvectormachine, drivingsimulator, simulationtraining, resultanalysis, particleswarmalgorithm

CLC Number:

DENG Qing, XUE Qing, LUO Jia. Analysis of Tank Driving Simulation Training Results Based on Support Vector Machine[J]. Acta Armamentarii, 2019, 40(9): 1953-1960.

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第40卷第9期
2019 年9月兵工学报ACTA
ARMAMENTARIIVol.40No.9Sep. 2019

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