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广义回归神经网络在卫星钟差短期预报中的应用

  • [设施]:长短波
  • [期刊/会议名称]:宇航计测技术
  • [摘要]:Neural Network(NN) has been widely used in forecast of nonlinear systems over the past years. Generalized Regression Neural Network(GRNN) ,which is a new kind of NN,is proposed to predict satellite clock error because of its nonlinear characteristic. An slip window pattern is used organize sample data,which can raise utilization rate of data. In order to improve forecast ability of GRNN,the method called K-fold Cross-Validation is employed to train network. Furthermore,the optimal smoothing factor is determined in terms of Root Mean Square Error(RMSE) . An experiment is carried out to verify GRNN effectiveness. Real satellite clock error data from International GNSS Service(IGS) is trained to construct the GRNN model,then this model is used to predict clock error. And also Clock error is forecasted using quadratic polynomial. Results show that GRNN model can reach ns-level prediction accuracy within 24 hour. Moreover,GNRR is more stable in comparison with quadratic polynomial. GRNN is worse than quadratic polynomial when predicting linear clock error,however,the former is obviously better than the latter when nonlinear clock error. Abstract:近年来,神经网络(Neural Network,简称NN)在非线性系统的预测方面取得了广泛的应用。考虑到卫星钟差包含了复杂的非线性因素,所以将一种新型神经网络-广义回归神经网络(Generalized Regression NeuralNetwork,GRNN)应用于钟差预报中。采用"滑动窗"方式构建样本数据以提高数据利用率,为提高网络的泛化能力,利用K重交叉验证法(K-fold Cross-Validation)对网络进行训练学习,并根据最小均方根误差(Root Mean SquareError,RMSE)确定最优平滑因子。利用国际GNSS服务(International GNSS Service,IGS)公布的精密GPS卫星钟差数据进行预报实验,并与传统二次多项式模型对比分析。结果表明:GRNN模型在24h的预报跨度内的误差可达ns级,并较多项式模型有更好的稳定性;对于线性钟差,GRNN模型要逊于多项式模型,而对于非线性钟差,GRNN模型则明显优于多项式模型,初步验证了GRNN用于钟差预报的可行性、有效性以及实用性。
  • [发表日期]:2013
  • [第一作者]:雷雨
  • [第一作者单位]:国家授时中心
  • [通讯作者]:雷雨
  • [通讯作者单位]:国家授时中心
  • [论文类型]:期刊论文
  • [期刊分类]:CSCD扩展版
  • [学科分类]:16075 时间测量学
  • [影响因子]:
  • [关键词]:Generalized regression neural network; Quadratic polynomial; Clock error prediction; Cross validation; Slip window
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