In this paper, we propose a Gated Recurrent Unit(GRU) neural network traffic prediction algorithm based on transfer learning. By introducing two gate structures, such as reset gate and update gate, the GRU neural network avoids the problems of gradient disappearance and gradient explosion. It can effectively represent the characteristics of long correlation traffic, and can realize the expression of nonlinear, self-similar, long correlation and other characteristics of satellite network traffic. The paper combines the transfer learning method to solve the problem of insufficient online traffic data and uses the particle filter online training algorithm to reduce the training time complexity and achieve accurate prediction of satellite network traffic. The simulation results show that the average relative error of the proposed traffic prediction algorithm is 35.80% and 8.13% lower than FARIMA and SVR, and the particle filter algorithm is 40% faster than the gradient descent algorithm.

本文提出了一种基于转移学习的门控循环单元神经网络流量预测算法。通过引入两种门结构，例如复位门和更新门，GRU神经网络避免了梯度消失和梯度爆炸的问题。它可以有效地表示长相关业务量的特征，并可以实现非线性，自相似，长相关等卫星网络业务量的表达。结合转移学习的方法，解决了在线交通数据不足的问题，采用粒子滤波在线训练算法，减少了训练时间的复杂性，实现了对卫星网络流量的准确预测。