To develop a high-accuracy method for predicting SCB based on the analysis of the shortcomings of the wavelet neural network (WNN) model, an improved WNN model to predict SCB is proposed herein. The activation function of the WNN is constructed by combining the advantages of Shannon and Gauss ‘window’ functions to improve the WNN. Finally, the improved WNN model is used to predict SCB. The results show that the proposed model has the highest prediction accuracy, stability, and robustness. Moreover, it effectively predicts long-time SCB data. Therefore, the proposed model can predict SCB with high accuracy.

中文翻译：

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基于变化率的改进小波神经网络预测卫星钟差

为了在分析小波神经网络(WNN)模型缺点的基础上，开发一种高精度的SCB预测方法，本文提出了一种改进的WNN模型来预测SCB。WNN 的激活函数是结合香农和高斯“窗口”函数的优点构建的，以改进 WNN。最后，使用改进的 WNN 模型来预测 SCB。结果表明，所提出的模型具有最高的预测精度、稳定性和鲁棒性。此外，它有效地预测了长时间的 SCB 数据。因此，所提出的模型可以高精度地预测 SCB。