Calibration prediction model of precision time base source based on neural network
YU Meixia, DONG Gang, XU Ruxun, YU Guoli
1. Gansu Institute of Metrology, Lanzhou 730050, China;
2. Mechatronics T & R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
Abstract: For the low efficiency and fixed period of precision time base calibration, a prediction model of precision time base calibration based on BP neural network is proposed to provide a new method for traceability calibration. Firstly, the prediction target influencing factors and calibration parameters generation mechanism of precision time base source are analyzed, and the time driven and data driven calibration prediction models are established. Secondly, based on BP neural network learning rules, the training and prediction data are normalized to avoid the adverse effects of distribution differences and data characteristics on the prediction model. Finally, different calibration prediction models are selected according to the prediction target. The simulation analysis shows that, the accuracy of time driven model is better than that of data driven model, and the prediction errors of the two prediction models are on the order of 10^–10, which meets the prediction accuracy requirements.
Keywords: precision time base source;BP neural network;prediction model;normalization
2023, 49(9):194-200  收稿日期: 2022-7-6;收到修改稿日期: 2022-10-24
基金项目: 甘肃省青年科技基金（23JRRA1270）；甘肃省市场监督管理局科技计划项目(SSCJG-JL-202101)
作者简介: 郁美霞（1989-），女，山东莒南县人，工程师，硕士，主要从事时间频率计量及状态预测方面的研究。
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