中文摘要：

为提高飞机重着陆判断的准确性,研究了将最小二乘支持向量机（Least square supportvector machine,LS-SVM）应用于民航飞机重着陆诊断的方法.首先,通过分析飞机着陆阶段的运动方程,确定了造成飞机重着陆的主要影响因素,将传统的单一指标诊断扩展到多指标诊断.然后,利用最小二乘支持向量机建立飞机重着陆诊断模型,采用遗传算法优化模型参数.训练和测试样本取自航空公司飞行品质监控数据库中相关参数值.与两类神经网络模型的比较表明,该方法具有更大的应用价值.

英文摘要：

To improve the accuracy of airplane＇s hard landing diagnosis,this paper develops a hard landing diagnosis model based on Least square support vector machine（LS-SVM）.Firstly,by analyzing airplane＇s motion equation of landing stage,the paper determines several major factors and expands diagnosis indexes from one index to several.Next,LS-SVM is used to establish the airplane＇s hard landing model.Then, genetic algorithm is used to optimize the model parameters of LS-SVM.We obtain the training and measuring data set from flight quality monitoring database of airlines.Finally,the paper compares with LS-SVM and two kind of neural network model.The results show that LS-SVM diagnosis model is feasible.