基于SCSO-GRU模型的网络流量预测

网络流量有实时性、不稳定性和时序相关性等特点，传统网络流量预测模型存在泛化能力不强和预测精度低等不足之处。为解决这些不足，本文提出一种结合基于正余弦的群优化（SCSO）算法的GRU神经网络的网络流量预测模型（SCSO-GRU）。首先，介绍SCSO算法的粒子更新原理；然后构建SCSO-GRU神经网络的网络流量预测模型，将SCSO算法用于模型训练，提高训练效果，克服传统GRU神经网络收敛于局部最优的缺点；最后用SCSO-GRU模型进行网络流量预测。实验结果表明，与传统LSTM和GRU模型相比，本文模型具有显著的收敛效果和较好的预测精度，可以更好地刻画网络流量变化趋势。

Network Traffic Prediction Based on SCSO-GRU Model

Network traffic has the characteristics of real-time, instability and correlation. The traditional prediction model of network traffic has the shortcomings of weak generalization ability and low prediction accuracy. To overcome these shortcomings, a network traffic prediction model (SCSO-GRU) based on GRU neural network combined with Sine-Cosine Swarm Optimization (SCSO) algorithm is proposed. Firstly, this paper introduces the particle update principle of SCSO algorithm. Then, it constructs a network traffic prediction model with SCSO-GRU neural network. The SCSO algorithm is used in model training to improve the training effect and overcome the disadvantage that the traditional GRU neural network converges to local optimum. Finally, this paper uses the SCSO-GRU model to predict the network traffic. The experimental results show that compared with the traditional LSTM and GRU models, the proposed model has better convergence efficiency and prediction accuracy, and can better describe the trend of network traffic.