| 利用自适应TS-IPSO优化的灰色系统预报卫星钟差 |
| |
| 引用本文： | 李成龙,陈西宏,刘继业,吴文溢,刘赞.利用自适应TS-IPSO优化的灰色系统预报卫星钟差[J].武汉大学学报(信息科学版),2018,43(6):854-859. |
| |
| 作者姓名： | 李成龙 陈西宏 刘继业 吴文溢 刘赞 |
| |
| 作者单位： | 军工程大学防空反导学院, 陕西 西安, 710051 |
| |
| 基金项目： | 国家自然科学基金61701525国家自然科学基金61671468 |
| |
| 摘 要： | 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6 h和24 h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6 h预报误差小于1.60 ns，24 h预报误差小于5.71 ns。
|
| 关 键 词： | 钟差 改进粒子群 灰色系统 预报 平滑处理 |
| 收稿时间： | 2016-05-08 |
Predicting Satellite Clock Errors Using Grey Model Optimized by Adaptive TS-IPSO |
| |
| Affiliation: | Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China |
| |
| Abstract: | 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. |
| |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
|
