关键词: 现代网络, 改进灰狼算法, 相空间重构, 历史样本数据, 深度学习网络, 全局最优参数

Abstract: Aiming at the parameter optimization problem of deep learning network in the process of network traffic prediction modeling, in order to improve the network traffic prediction results, we proposed a network traffic prediction model based on improved gray wolf algorithm to optimize the deep learning network. Firstly, the historical data of network traffic was collected and preprocessed by phase space reconstruction and normalization. Secondly, gray wolf algorithm was introduced to quickly search the relevant parameters of the global optimal deep learning network, the preprocessed historical data of network traffic was learned according to the optimal parameters, and a prediction model that could mine the change law of historical data of network traffic was established. Finally, the network traffic prediction model of deep learning network optimized by other algorithms was compared and analyzed. The experimental results show that the network traffic prediction accuracy based on improved gray wolf algorithm to optimize deep learning network is more than 90%, which is much higher than other comparison models, and the modeling time of prediction modeling process is less than that of comparison model, which can meet the requirements of high accuracy and real-time of network traffic management.

Key words: modern network, improved gray wolf algorithm, phase space reconstruction, historical sample data, deep learning network, global optimal parameter