Li Xiaoyu, Li Shaobo, Zhou Peng, Chen Guanglin
College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China.
Entropy (Basel). 2022 Mar 29;24(4):478. doi: 10.3390/e24040478.
In this paper, we propose a broad learning system based on the sparrow search algorithm. Firstly, in order to avoid the complicated manual parameter tuning process and obtain the best combination of hyperparameters, the sparrow search algorithm is used to optimize the shrinkage coefficient (r) and regularization coefficient (λ) in the broad learning system to improve the prediction accuracy of the model. Second, using the broad learning system to build a network interface flow forecasting model. The flow values in the time period [T-11,T] are used as the characteristic values of the traffic at the moment T+1. The hyperparameters outputted in the previous step are fed into the network to train the broad learning system network traffic prediction model. Finally, to verify the model performance, this paper trains the prediction model on two public network flow datasets and real traffic data of an enterprise cloud platform switch interface and compares the proposed model with the broad learning system, long short-term memory, and other methods. The experiments show that the prediction accuracy of this method is higher than other methods, and the moving average reaches 97%, 98%, and 99% on each dataset, respectively.
在本文中，我们提出了一种基于麻雀搜索算法的广义学习系统。首先，为了避免复杂的手动参数调整过程并获得超参数的最佳组合，使用麻雀搜索算法优化广义学习系统中的收缩系数（r）和正则化系数（λ），以提高模型的预测精度。其次，利用广义学习系统构建网络接口流量预测模型。将时间段[T - 11,T]内的流量值用作时刻T + 1的流量特征值。将上一步输出的超参数输入网络，训练广义学习系统网络流量预测模型。最后，为了验证模型性能，本文在两个公共网络流量数据集以及一个企业云平台交换接口的实际流量数据上训练预测模型，并将所提出的模型与广义学习系统、长短期记忆等方法进行比较。实验表明，该方法的预测精度高于其他方法，在每个数据集上的移动平均分别达到97%、98%和99%。
