粒子群算法优化的灰色神经网络卫星钟差预报

Satellite clock error prediction based on gray neural network optimized by particle swarm algorithm

为了进一步提高导航卫星钟差预报的精度,结合粒子群算法与灰色神经网络的特点,提出一种粒子群算法优化的灰色神经网络模型:利用粒子群算法寻优灰色神经网络的权值与阈值,以提高预报的精度;并选取IGS提供的精密钟差数据,分别采用灰色模型、灰色神经网络模型与本文模型进行对比分析。实验结果表明:该模型预测精度高、稳定性强、模型可靠,能够有效进行卫星精密钟差预报。

In order to further improve the prediction accuracy of navigation satellite clock errors,the paper proposed an improved gray neural network model based on particle swarm algorithm combining the characteristics of particle swarm algorithm and gray neural network：particle swarm algorithm was used to optimize the weights and thresholds of gray neural network for increasing the prediction accuracy,and the comparative analysis among gray model,gray neural network model and the proposed model was carried out by selecting the precise clock error data provided by IGS.Experimental result showed that the proposed model could predict the precise clock errors of satellite efficiently with high prediction accuracy and good stability and reliability.