| 基于粒子群优化核极限学习机的北斗超快速钟差预报 |
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| 引用本文： | 李文涛,边少锋,任青阳,梅长松,潘雄.基于粒子群优化核极限学习机的北斗超快速钟差预报[J].宇航学报,2019,40(9):1080-1088. |
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| 作者姓名： | 李文涛 边少锋 任青阳 梅长松 潘雄 |
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| 作者单位： | 1.中国地质大学(武汉)地理与信息工程学院，武汉 430074; 2.重庆交通大学土木工程学院，重庆 400074 |
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| 基金项目： | 国家自然科学基金(41874009,41476087); 国家重点研发计划(2016YFC0802206-3) |
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| 摘 要： | 针对卫星钟差序列中非线性特性较为复杂和超快速钟差预报精度较低的问题，将核极限学习机算法引入到北斗超快速钟差预报中。首先，将极限学习机进行优化，引入粒子群优化算法来选择核极限学习机所需的核参数和正则化参数；然后，将优化后的方法应用到超快速钟差预报中，并给出了利用该方法进行超快速钟差预报的步骤；最后，在分析iGMAS提供的实测北斗超快速钟差数据的基础上，选用单天和多天数据进行短期预报。结果表明：在短期预报6h范围内，利用本文提供的优化方法解算得到的超快速钟差预报精度明显优于二次多项式模型和周期项模型，并且采用此方法得到的超快速钟差预报产品与iGMAS提供的超快速钟差预报产品(ISU-P)相比，GEO、IGSO和MEO卫星的预报精度分别提升了50.51%、46.98%、40.67%，其与最终精密钟差的符合程度显著 增强 。
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| 关 键 词： | iGMAS 北斗超快速钟差预报 核极限学习机 粒子群优化 最终精密钟差 |
| 收稿时间： | 2019-01-07 |
Kernel Extreme Learning Machine Based on Particle Swarm Optimization for Prediction of Beidou Ultra Rapid Clock Offset |
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| LI Wen tao,BIAN Shao feng,REN Qing yang,MEI Chang song,PAN Xiong.Kernel Extreme Learning Machine Based on Particle Swarm Optimization for Prediction of Beidou Ultra Rapid Clock Offset[J].Journal of Astronautics,2019,40(9):1080-1088. |
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| Authors: | LI Wen tao BIAN Shao feng REN Qing yang MEI Chang song PAN Xiong |
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| Affiliation: | 1.School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; 2.School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China |
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| Abstract: | Aiming at the problems of the complexity lying in the nonlinear characteristics of the satellite clock offset sequence and the lower accuracy of the ultra-rapid clock offset prediction, the kernel extreme learning machine algorithm is introduced into the Beidou ultra-rapid clock offset prediction. Firstly, the extreme learning machine is optimized, and the particle swarm optimization algorithm is introduced to select the kernel parameters and regularization parameters required by the kernel extreme learning machine. Then, the optimized method is applied to the ultra-rapid clock offset prediction, and the steps of using this method for the ultra-rapid clock offset prediction are given. Finally, based on the analysis of the measured Beidou ultra-rapid clock data provided by iGMAS, the data for a single day and multiple days are selected for short term prediction. The results show that in the short term prediction within 6h, the ultra-rapid clock offset prediction accuracy obtained from the optimization method adopted in this paper is obviously superior to the quadratic polynomial model and the periodic term model. Moreover, regarding the ultra-rapid clock difference prediction product obtained by this method, the prediction accuracies of GEO, IGSO and MEO satellites increase by 50.51%, 46.98% and 40.67% respectively, and their compliance with the final precision clock offset is significantly enhanced, compared with the ultra-rapid clock difference prediction product (ISU-P) provided by iGMAS. |
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| Keywords: | iGMAS Beidou ultra-rapid clock offset prediction Kernel extreme learning machine Particle swarm optimization Final precision clock offset |
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