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THE CLOCK DIFFERENC PREDICTION OF ATOMIC CLOCK BASED ON ELMAN NEURAL NETWORK

机译:基于ELMAN神经网络的原子钟钟差预测

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摘要

The clock difference prediction of the atomic clock is one of the parts in constructing the atomic time scale and controlling the time scale, so it is very important to explore high-precision clock difference prediction. As a feedback neural network that can dynamically remember, Elman neural network is very suitable for time series prediction. This paper proposes an algorithm of clock difference prediction based on Elman neural network, and uses the relevant data of the hydrogen clocks and cesium clocks from National Institute of Metrology (China) to conduct experiments in order to research the effectiveness of the algorithm. The experimental results show that the prediction algorithm which is proposed in this paper improves the prediction accuracy of clock difference compared with the prediction algorithm of linear regression, the prediction algorithm of support vector machine and the least square support vector machine.
机译:原子钟的时差预测是构建原子时标和控制原子时标的一部分,因此探索高精度的时差预测具有重要意义。Elman神经网络作为一种能够动态记忆的反馈神经网络,非常适合于时间序列预测。本文提出了一种基于Elman神经网络的钟差预测算法,并利用中国计量科学研究院氢钟和铯钟的相关数据进行了实验,以研究该算法的有效性。实验结果表明,与线性回归预测算法、支持向量机预测算法和最小二乘支持向量机预测算法相比,本文提出的预测算法提高了时钟差的预测精度。

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