基于灰色模型和最小二乘支持向量机的卫星钟差预报  

Prediction of Satellite Clock Errors with a Combination of the Gray Model and the Least-Squares Support Vector Machines

作　　者：雷雨[1,2,3] 赵丹宁[1,4,3] 

机构地区：[1]中国科学院国家授时中心,陕西西安710600 [2]中国科学院时间频率基准重点实验室,陕西西安710600 [3]中国科学院大学,北京100049 [4]中国科学院精密导航定位与定时技术重点实验室,陕西西安710600

出　　处：《天文研究与技术》2014年第1期39-45,共7页Astronomical Research & Technology

基　　金：国家自然科学基金(10573019)资助

摘　　要：提出一种联合灰色模型(Grey Model,GM)和最小二乘支持机(Least-Squares Support Vector Machines,LS-SVM)回归算法的卫星钟差智能组合预报方法。首先根据历史钟差数据建立不同的GM(1,1)灰色模型,然后利用这些模型进行钟差预报,最后采用最小二乘支持向量机回归算法对不同GM(1,1)模型的预报结果进行非线性组合,以获得最终预报值。该方法在充分利用灰色模型所需原始数据少、建模简单等优点的基础上,结合最小二乘支持向量机所具有的小样本、非线性、泛化能力强等特性,提高了预报可靠性和精度。实例验证了该组合方法的可行性、有效性和实用性。We propose a new method for predicting satellite clock errors. The new method combines the Gray Model( GM) and the Least-Squares Support Vector Machines( LS-SVM) regression algorithm. The method first builds different GM( 1,1) models based on observational data of satellite clock errors. Future satellite clock errors are predicted from these models. The LS-SVM regression algorithm is employed to combine the prediction results of the GM( 1,1) models in a nonlinear manner. This method keeps the advantages of the GM( 1,1) models such as relatively small amounts of required observational data and the simplicity in data modeling,and in the mean time,it has all the useful features of the LS-SVM,including relatively few samples to be used,nonlinearity,and easy generalization. Therefore,the method improves the reliability and accuracy of prediction results over previous methods. Our experimental results show that the new method is feasible,effective,and practical.

关 键 词：导航定位 卫星钟差 组合预报 灰色模型 最小二乘支持向量机 

分 类 号：P228.41[天文地球—大地测量学与测量工程]