基于SVM的导弹自由飞行阶段可靠性评估

Reliability Evaluation for Missile Free Flight Based on SVM

为更好地评估巡航导弹自由飞行阶段的可靠性,对小样本回归问题进行研究。首先对实验数据进行特征选择与提取得到学习样本,在此基础上利用支持向量机(support vector machine,SVM)方法进行可靠性评估研究,然后通过仿真实验对比神经网络与支持向量机2种方法的评估效果。结果证明:SVM的训练学习效率更高,同时能够保证较好的泛化性能,提高自由飞行阶段可靠性的评估效果。

In order to improve the performance of the missile reliability estimation in free flight phase, the research of the regression problem with small scale of samples is carried out. Firstly, the learning samples are obtained after feature selection and abstraction, based on which the SVM is used to estimate the reliability of the missiles. Then the estimation performance of the neural network and SVM is compared by simulation. The results indicate that the efficiency of SVM was higher than the neural network, and SVM also has good generalization ability and can improve the performance of the reliability estimation.