摘要
导弹气动模型是控制系统设计、系统仿真、性能评估的前提和基础。如何从飞行试验数据中获取对导弹气动特性的准确描述是导弹气动力建模方面的一个重要问题。本文将K-最近邻算法引入靶试数据气动力辨识分析,并针对数据特点采取了相应的改进措施,建立了基于气动力辨识数据的气动力模型,为飞行试验数据的应用拓宽了思路。验证结果表明,K-最近邻模型预测结果接近气动力真值,从而证明了方法的可行性。
Missile aerodynamic model is a key basis for the control system design,system simulation and performance evaluation,and an important problem related to the aerodynamic modeling is how to acquire accurate description of aerodynamic characteristics for the flight-test data.This paper leads the KNN（K Nearest Neighbor）algorithm into the aerodynamic identification analysis of the flight-test data,and takes the relative improvement measures to the data to provide a new methodology for the flight-test data application.The validation results show that the KNN model can accurately predict the missile aerodynamic coefficient.It is proved that this method is feasible.
出处
《航空兵器》
2011年第3期7-10,共4页
Aero Weaponry
关键词
气动辨识
实例学习
K-最近邻
aerodynamic identification
instance-based learning
K-nearest neighbor
