PSO-LSSVM在民机气动性能数学建模上的应用

目前风洞试验仅为民用飞机飞行性能提供有限数据.全飞行包线的技术支持对于民机飞行试验十分重要,需要采用数学建模和参数辨识的方法.选择合适的机器学习算法是参数辨识中最为关键的一步.支持向量机(SVM)采用结构风险最小化原理,尤其适用于小样本情形.根据A320非巡航起降阶段的几组真实数据,以及全机气动力估算的结果,使用最小二乘支持向量机建立预测模型.随后采用粒子群算法优化模型参数从而提升泛化能力.由此实现民机飞行包线的气动性能整体建模与辨识.与Ma=0.78时的实验数据相比较,PSO-LSSVM模型的预测结果吻合,是一种有效的气动数学建模方法.

Aerodynamics Calibration of Civil Airplane with PSO-LSSVM

The data provided by wind tunnel tests by far are not nearly sufficient when studying the aerodynamic performances of civil airplanes,which accordingly calls for technical support by means of mathematical modeling and parameter identification.Support vector machine provides effective tool concerning small sample data,as it embodies structural risk minimization principle.Taking A320 for example,a prediction model is built up employing least square support vector machine (LS-SVM) method,according to its whole plane aerodynamic parameters estimated by empirical formulae and several real test data at non-cruising phases.Particle Swarm Optimization (PSO) method is later applied to improve the model,which enables it better generalization properties.Thus,the overall modeling and identification of aerodynamic parameters come into being.Comparing with the test data when Ma=0.78,PSO-LSSVM model shows good predicting performance,and can be adopted as a practical way of aerodynamic calibration.