摘要
针对军事系统作战效能预测问题,采用基于支持向量回归的指标权重挖掘方法,通过比较偏导确定影响作战效能的关键因素,将优化后的效能指标和效能值分别作为模型的输入和输出,建立基于Elman神经网络的效能预测模型。并将其应用于C4ISR系统的动态作战效能预测分析中。结果表明,该方法能够减少不确定因素的影响,在一定程度上降低了预测模型的复杂度,为科学预测军事系统作战效能提供了有效的技术支撑。
To deal with combat effectiveness prediction of the military system, a SVR-based crucial evaluation indexes mining method was carefully investigated. The key indexes in the effectiveness evaluation were found by comparing partial derivatives. The model of efficiency prediction based on Elman neural networks, which used effective optimized indexs and values as input and output, was exerted to combat effectiveness prediction of C4 ISR. The results show that the method can reduce the complexity of prediction model, and avoid uncertain factors existing in system, which provide effective technical support for the combat effectiveness prediction scientifically.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第1期43-49,共7页
Journal of System Simulation
