基于改进GRU算法的天基信息网资源预测研究

Research on Resource Prediction of Space-based Information Network Based on Improved GRU Algorithm

为了有效地提高天基信息网中的资源利用效率,提出了一种基于改进GRU(gated recurrent unit)算法的天基信息网资源预测模型.首先,提出递阶式三级架构的资源预测框架来解决天基环境长时延的问题;然后,采用Adam优化器优化GRU网络的学习速率;最后,引入Dropout技术解决网络中存在的过拟合问题.实验仿真了不同预测模型下对各种天基资源的预测,同时对比不同优化器作用下GRU模型的预测准确率,结果表明,基于改进GRU网络的资源预测模型具有更好的性能.

In orde to improve the resource utilization of space-based information metwork efficiency,a resource prediction model of space-based information network was presented based on the improved GRU(gated recurrent unit)algorithm.Firstly,a hierarchical three-level resource prediction framework was proposed to solve the problem of long delay in space-based environment.Then,Adam optimizer was used to optimize the learning rate of GRU network.Finally,Dropout technology was introduced to solve the over-fitting problem in the network.The experiments simulated the prediction of various space-based resources under different prediction models,and compared the prediction accuracy of GRU model under different optimizers.The results show that the resource prediction model based on improved GRU network has better performance.