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An Artificial Neural Network Model for Timescale Atomic Clock Ensemble Algorithm
MAPAN ( IF 1 ) Pub Date : 2020-12-09 , DOI: 10.1007/s12647-020-00414-0
R. Sruthikeerthi Nandita , Shikha Maharana , B. Rajathilagam , T. Subramanya Ganesh , Subhalakshmi Krishnamoorthy

Atomic clocks work on a standard frequency generated by the electron transitions in the atoms of the core material. A timescale is a reference frequency and phase measure generated by a set of atomic clocks. An ensemble algorithm combines the participating atomic clocks to form a “perfect” clock. The perfect clock is very stable and precise in terms of frequency and phase. There are many methods that exist to develop an ensemble for a timescale such as Kalman filter-based algorithms, inverse Allan variance-based algorithms, etc. A neural network-based realization of the ensemble algorithm for a timescale is discussed in this paper. The artificial neural network (ANN) model dynamically adapts the weights of the clocks to accommodate the behavioural changes in the clocks. This paper uses different types of M-sample deviations like overlapping Allan deviation and overlapping Hadamard deviation as the inputs to the model.



中文翻译:

时标原子钟集成算法的人工神经网络模型

原子钟在由核心材料原子中的电子跃迁产生的标准频率上工作。时标是由一组原子钟生成的参考频率和相位度量。集成算法将参与的原子钟组合成一个“完美”钟。完美的时钟在频率和相位方面非常稳定和精确。存在许多开发时间尺度的集合的方法,例如基于Kalman滤波器的算法,基于逆Allan方差的算法等。本文讨论了基于神经网络的时间尺度的集合算法的实现。人工神经网络(ANN)模型可以动态调整时钟的权重,以适应时钟的行为变化。

更新日期:2020-12-09
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