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Enhancement of Artillery Simulation Training System by Neural Network  

Ryu, Hai-Joon (고려대학교 정보경영공학전문대학원 정보경영공학과)
Ko, Hyo-Heon (고려대학교 산업시스템정보공학과)
Kim, Ji-Hyun (고려대학교 정보통신연구소)
Kim, Sung-Shick (고려대학교 산업시스템정보공학과)
Publication Information
Journal of the military operations research society of Korea / v.34, no.1, 2008 , pp. 1-11 More about this Journal
Abstract
A methodology for the improvement of simulation based training system for the artillery is proposed in this paper. The complex nonlinear relationship inherent among parameters in artillery firing is difficult to model and analyze. By introducing neural network based simulation, accurate representation of artillery firing is made possible. The artillery training system can greatly benefit from the improved prediction. Neural networks learning is conducted using the conjugate gradient algorithm. The evaluation of the proposed methodology is performed through simulation. Prediction errors of both regression analysis model and neural networks model are analyzed. Implementation of neural networks to training system enables more realistic training, improved combat power and reduced budget.
Keywords
Artillery; Neural Networks; Simulation Trainging System;
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