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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.
In order to improve the accuracy of aircraft landing judgment, a method of applying Least Square Support Vector Machine (LS-SVM) to civil aircraft landing recognition is studied.Firstly, by analyzing the equations of motion during the landing phase, The main influencing factors of aircraft landing are identified, and the traditional single index diagnosis is extended to multi-index diagnosis.At last, the least squares support vector machine (SVM) is used to establish the airplane landing landing diagnosis model and the genetic algorithm is used to optimize the model parameters.Training and testing samples Which is taken from the related parameter values in airline quality monitoring database.The comparison with two neural network models shows that the method has more application value.