利用自适应TS-IPSO优化的灰色系统预报卫星钟差

Predicting Satellite Clock Errors Using Grey Model Optimized by Adaptive TS-IPSO

在传统灰色系统预报模型的基础上,提出了一种自适应双子群改进粒子群算法（improved particle swarm optimization algorithm by two subgroups,TS-IPSO）和灰色系统相结合的预报模型。首先对钟差序列进行平滑性检验,对不满足平滑条件的序列作对数平滑处理;然后对灰色系统模型进行优化,为避免粒子群算法陷入局部最优,建立了主辅子群协同进化,惯性权重非线性递减机制。通过TS-IPSO优化发展灰数和内生控制灰数,增强了灰色系统模型的泛化能力。选取来自4种不同钟型的卫星钟差数据进行计算分析。结果表明,模型对6h和24h的预报精度和稳定性均优于传统模型,特别是对短期稳定性较差的铯钟,实现了6h预报误差小于1.60ns,24h预报误差小于5.71ns。

This paper proposes a combination prediction model based on improved particle swarm optimization algorithm by two subgroups（TS-IPSO）and grey model.We check the smoothness of clock bias sequence,and log it if the smoothness is not satisfied.To avoid getting stuck at local optimization and turning premature convergence,we established a mechanism so that the main particle swarm and assistant swarm search synergistically,so the inertia weight decreases nonlinearly.We use TS-IPSO to optimize development obscure number and endogenous control obscure number,thus the improved grey model can adapt and gain higher precision.Satellite data from four different clocks are selected and calculated,the results show that the improved model is superior to the conventional model,in precision and stability,for 6-hour and 24-hour prediction.Especially,in the Cs clock,it achieves 6-hour prediction errors of less than 1.60 ns,and 24-hour prediction errors of less than 5.71 ns.