运载火箭风场高精度拟合方法

针对运载火箭风场传统拟合方法精度低的问题，提出一种基于最少参数神经网络的高精度拟合方法。该方法将运载火箭飞行高度作为网络输入，将风场的速度和朝向作为网络输出，在满足精度要求下使用最少的网络层数和神经元个数完成风场拟合，并给出了隐层节点数的下界公式。和传统的最小二乘多项式拟合及其分段形式相比，最少参数神经网络只使用一套框架即可完成风速和风向的拟合，并提高了拟合精度。大量仿真结果充分说明了所提出方法的有效性、简洁性和鲁棒性。

High precision Fitting Approach for Wind Field of Launch Vehicles

Aiming at the problem of low accuracy of traditional fitting method for the wind field of launch vehicles, a high precision fitting approach based on least parameter neural network is proposed. In this method, the flight altitude of launch vehicles is taken as the network input, and the wind field velocity and orientation are taken as the network outputs. The wind field fitting is completed with the minimum number of network layers and the number of neurons, and the lower bound formula of the number of hidden layer nodes is given. Compared with the traditional least square polynomial fitting and its multi segment style, the least parameter network fitting can improve the precision with a unified framework. A large number of simulations results fully demonstrate the effectiveness, conciseness and robustness of the proposed method.