遗传算法优化BP神经网络的网络流量预测

为了提高网络流量预测精度,提出一种基于遗传算法优化BP神经网络的网络流量预测模型(GA-BPNN)。首先采集网络流量数据,并进行相应预处理,然后将网络流量训练样本输入到BP神经网络进行学习,并采用遗传算法对BP神经网络参数进行优化,最后采用建立的网络流量预测模型对网络流量测试集进行预测,并通过仿真实验对模型性能进行测试。结果表明,GA-BPNN提高了网络流量的预测精度,获得比较理想的网络流量预测结果。

Network Traffic Prediction Model based on Genetic Algorithm Optimizing BP Neural Network

In order to improve the prediction accuracy of network traf ic, a network traf ic prediction model based on genetic algorithm optimizing BP neural network is proposed in this paper. Firstly, the network traf ic data is col ected, and carries the corresponding pretreatment, and then training samples of network traf ic are input into BP neural network to learn, and genetic algorithm is used to optimize parameters of BP neural network, final y, the network traf ic prediction model is established, and the simulation results is used to test the performance of model. The results show that the proposed model can improve the prediction accuracy of network traf ic and can obtain good network traf ic prediction results.