基于知识神经网络的运载火箭弹道选择

在火箭发射中,往往有多组来自不同设备的测量弹道,进行可靠的火箭弹道选择是飞行状态监视和安控判断的前提.考虑到火箭飞行过程和测量环境的复杂性,提出了一种基于知识神经网络的运载火箭弹道选择方法.把火箭飞行弹道的先验知识和弹道样本的学习训练相结合,先利用数据的连续性排除跳变弹道,然后计算弹道间的差异,最后采用一个神经网络判断各条弹道的可靠性,并结合测量设备精度进行选优.实验和仿真表明了这种方法的可靠性和有效性.

Craft Orbit Selection by Knowledge Based Neural Network

There are more than one measure devices during the craft launching proceeding, reliable craft orbit measurement selection is important for launching monitor and safety control. This paper proposes a novel orbit selection method by knowledge based neural network, which combines the human knowledge and orbit samples based training together. The false spur orbit data are deleted according the data continuity rule at first, then the difference between orbit data is computed, and at last a neural network is trained to test reliability of each data and select the orbit with high precision among all orbit data. Simulations and experiments show the efficiency and reliability of our method.