时钟校准过程中的组合钟差预报模型

摘要

时间同步技术在分布式系统中应用广泛,时钟校准是时间同步的前提。针对时间校准过程中所需的时间信号可能出现传递失效等问题,在时间校准过程中引入钟差预报技术。同时为提高钟差预报模型的预报精度,提出一种GM�BP神经网络的钟差预报组合模型,首先利用已测钟差数据建立多个不同维数的GM(1,1)钟差预报模型,并对某时段的钟差进行预报,发挥多个不同GM(1,1)模型的优点；然后利用训练好的 BP 神经网络对预报结果进行非线性组合,最终的预报结果为BP神经网络非线性组合后的钟差。利用衰减器模拟对流层散射信道,设计对流层散射单向时钟校准试验,利用试验过程中实测的钟差数据进行组合模型精度验证。仿真结果表明,组合模型较单一预报模型,预报误差更加平稳,精度上提高53%~95%。%Time synchronization has been widely used in the distributed system.Clock calibration is the pre-condition of the time synchronization.Aimed at the problem that in the process of the invalidation of time signal's transmission in the calibration may exist in time synchronization,this paper introduces a clock er-ror prediction into the synchronization system.Simultaneously,to improve precision of error prediction, the paper proposes a model based on GM-BP network.In the combined model,different GM (1,1)mod-els based on observational data of clock errors are built.The models can predict the future clock errors. The paper employs the BP network to combine the prediction results in a nonlinear manner.The time sig-nals transfer via troposcatter imitated by an attenuator to prove the precision of the combined model.The result shows that the accuracy of the combined model is superior to the single model,the error of the new model has a better stability and the precision increases by 53%~95%.