基于NRS-SVM模型的航空弹药消耗预测研究

结合航空弹药训练消耗的特点,研究邻域粗糙集(Neighborhood rough sets,NRS)与支持向量机(Support vector machines,SVM)融合的航空弹药训练消耗预测问题。通过邻域粗糙集将5个初始影响因素约简为3个核心影响因素,并以此训练集对支持向量机进行回归优化。通过参数寻优得到最优的惩罚参数和核参数,进而构建NRS-SVM组合预测模型来预测航空弹药消耗。实证研究表明,该模型预测结果与实际数据吻合度较高,且与其他预测模型相比具有更好的预测性能。

Research on Aviation Ammunition Consumption Prediction Based on NRS-SVM Model

Combining with the characteristics of aviation ammunition training consumption, this paper studies the forecasting of aviation ammunition training consumption based on the fusion of neighborhood rough sets (NRS) and support vector machines (SVM). The five initial influencing factors are reduced to three core influencing factors by using NRS and the training set is used to optimize SVM. The best penalty parameter and kernel parameter are obtained through parameter optimization and then the NRS-SVM combination forecasting model is constructed to forecast the training consumption of aviation ammunition. Empirical studies show that predictions using the model are in good agreement with the actual data and the model has better predictive performance than the other prediction models.