Atomic clock frequency difference prediction algorithm based on genetic wavelet neural network

Abstract

Atomic clock frequency difference prediction is the key step in atomic clock time scale calculation and atomic clock control. Precise prediction algorithm can accurately predict the future operation state of atomic clock which can be used to improve the accuracy of atomic time. In order to further improve the prediction accuracy of atomic clock frequency difference, a genetic wavelet neural network (GAWNN) atomic clock frequency difference prediction algorithm is proposed in this paper, which is based on wavelet neural network (WNN) atomic clock frequency difference prediction algorithm. The genetic algorithm is used to optimize the wavelet neural network so as to select the appropriate number of hidden layers and the number of training points to construct the atomic clock frequency difference prediction model. In this paper, the algorithm is validated by the hydrogen clock and cesium clock actual frequency difference data of the National Institute of Metrology, and the results show that the algorithm improves the prediction accuracy of hydrogen clock and cesium clock frequency difference data.