基于RBF网络的发动机起动过程的模型辨识

针对航空发动机在起动过程中，各截面气流处于亚临界状态，难以利用传统的气动热力学方法进行建模的问题，本文利用某型飞机的飞参记录的发动机起动过程的数据作为学习样本，采用径向基（RBF）神经网络的方法，建立了该型发动机起动过程动态模型。仿真结果表明，该方法具有动态性好，精度较高的优点，开辟了发动机中小转速建模的新途径。

An Identification Model of Aeroengine Starting Process Based on RBF Network

In the process of engine starting, the airflow of every section is in subcritical condition. It's hard to construct a model by using the traditional thermodynamics method. A dynamic identification model is set up in this paper based on Radial Basis Function network, using the flight data records of aeroengine parameter in the process of engine starting as learning samples. The simulation results show that the model has a good dynamic performance and high accuracy, which opens a new way to build the model of engine in low speed.