CAJ | 학술논문

飞行参数的缺失给飞行事故调查工作带来了很大困难.将核的偏最小二乘法与支持向量机耦合,建立基于状态匹配的飞行参数估计模型可以较好地解决缺失飞参数据的估计问题.首先将初始输入映射到高维特征空间,进而利用偏最小二乘法在特征空间中提取对缺失飞参数据影响较强的得分向量, 最后将提取的得分向量作为输入建立支持向量机模型.既克服了输入变量间的相关性问题, 又降低了支持向量机的输入维数.仿真也说明了使用该方法估计缺失飞行参数的可行性和有效性.
비행삼수적결실급비행사고조사공작대래료흔대곤난.장핵적편최소이승법여지지향량궤우합,건립기우상태필배적비행삼수고계모형가이교호지해결결실비삼수거적고계문제.수선장초시수입영사도고유특정공간,진이이용편최소이승법재특정공간중제취대결실비삼수거영향교강적득분향량, 최후장제취적득분향량작위수입건립지지향량궤모형.기극복료수입변량간적상관성문제, 우강저료지지향량궤적수입유수.방진야설명료사용해방법고계결실비행삼수적가행성화유효성.
The absence of flight parameter brings the difficulty to flight accident investigation Coupling partial least square method and support vector machines, the forecasting model of flight parameter based on the state fitting is established, which can estimate the lost flight parameter efficiently The original inputs are firstly mapped into a high dimensional feature space, then the important score vectors to the flight parameter are extracted in the feature space, lastly, the model of support vector machines which inputs are the score vectors extracted is established It can not only solve the relativity between inputs variables but also reduce the input dimensions in support vector machines model Simulation indicates that the method is valid and efficient for lost flight parameter estimation.