基于特征加权K最近邻的无人机武器发射过程参数预测  

Prediction of UAV Missile Launch Parameter Extraction Based on Feature Weight K-nearest Neighbour Algorithm

作　　者：王改堂 王斐[2] 黄超凡 丁力 叶锦函 

机构地区：[1]中国兵器工业第203研究所,西安710065 [2]河南科技大学信息工程学院,河南洛阳471003

出　　处：《弹箭与制导学报》2014年第4期41-42,46,共3页Journal of Projectiles,Rockets,Missiles and Guidance

摘　　要：针对K最近邻算法难以建立高精度的非线性模型问题,提出了一种基于特征加权的K最近邻预测方法。为提高模型的预测精度,该方法从特征重要程度的角度出发,采用Bootstrap特征加权方法对K最近邻算法进行特征加权。为了验证该方法的有效性,对无人机武器发射过程参数进行了预测。实验结果表明,与其它算法相比,该算法不仅体现了样本数据在模型中的作用,而且具有较高的预测精度。A novel algorithm based on feature weighted KNN was proposed to solve the problem that it is hard to build nonlinear system modeling for KNN algorithm with high predicting precision. In order to improve prediction accuracy of the model, the Bootstrap feature weight method was used in KNN algorithm in view of feature importance of samples. The proposed method predicts UAV missile launch pa- rameter extraction to verify effectiveness of the method. The simulation results show that the proposed algorithm not only shows role of sam- ples in the model, but also has the advantages of high prediction accuracv compared with the other methods.

关 键 词：特征加权 K最近邻 无人机 武器 

分 类 号：V279[航空宇航科学与技术—飞行器设计]