基于灰色粒子群神经网络的航天器参量预测

Prediction Based on Particle Swarm Optimization-Grey Neural Network of Spacecraft Parameter Values

针对航天器精确预测与健康管理的需求,将粒子群算法、灰色理论与神经网络的优势相结合,提出了一种灰色粒子群神经网络组合参量预测方法,实现了灰色模型、粒子群算法、神经网络模型的优势互补。针对某卫星南帆板输出电流参量的预测实例,采用总平均绝对误差、总平均绝对百分比误差、总均方根误差3个预测结果评价指标,对灰色粒子群神经网络模型、粒子群神经网络模型、灰色模型和残差修正灰色模型的预测结果进行了比较,结果证明灰色粒子群神经网络模型的预测精度较高,在航天器参量预测领域具有很好的应用前景。

Aiming at the requirements of precise prediction and health management of spacecraft, a method for combinational prediction of parameter values called particle swarm optimization-grey neural network is promoted. The method enables particle swarm optimization algorithms, grey theory and neural network to complement each other. Firstly, a prognosis for output current values of southern sailboard of a certain satellite is taken as an example. Then, three evaluation indexes of prediction, including mean absolute error, mean absolute percentage error and root mean square error, are chosen to evaluate the results of different step-length prediction is of particle swarm optimization-fuzzy neural network. The results show that the particle swarm optimization-fuzzy neural network is effective. Secondly, the mean absolute percentage errors of particle swarm optimization-fuzzy neural network, grey model-particle swarm optimization neural network, particle swarm optimization neural network and grey model are calculated. The results show that the model of particle swarm optimization-fuzzy neural network is the most precise one and more efficient in prediction than others. It has vast application prospects in the field of prediction of spacecraft parameter values.