基于特征加权支持向量机的武器系统效能评定方法

Assessment method of weapon system effectiveness based on support vector machine with weighted feature

针对武器系统效能综合评定的任务需求,同时考虑到传统特征选择方法的不足,提出采用一种监督型封装模式的加权特征选择方法用于武器系统的效能评定,能有效区分不同特征对效能的影响程度。鉴于支持向量机的良好应用效果以及混合核函数的优良性能,采用基于混合核函数的支持向量机建立特征与效能之间的关系模型。考虑到特征选择和评估模型建立的目标一致性,提出一种基于粒子群优化算法的联合优化方法,同时实现特征的影响程度分析和效能评估模型的优化。最后以导弹系统的效能评定问题为背景开展应用分析,实际计算表明所提出方法的有效性。

The requirement of weapon tactical and technical indicators were taken system effectiveness assessment and the importance analysis of into account, moreover, considering the shortcomings of the traditional method of feature selection, A weighted feature selection with the supervised wrapper mode was used in the effectiveness assessment of weapon system, which can effectively distinguish the influence of different features on the system effectiveness. In view of the good application effects of support vector machine（SVM） ,as well as a good performance of the mixture of kernels, the relationship model among the features and the system effectiveness was established based on SVM with the mixture of kernels. In addition, considering the consistency of feature selection and the establishment of effectiveness assessment model, a joint optimization method based on particle swarm optimization（PSO） was adopted, which can synchronically achieve the influence analysis of features and the optimization of effectiveness assessment model. Experiments show that the proposed method is effective.