摘要
为了提高网络流量预测精度,利用相空间重构和预测模型参数间的相互联系,提出一种遗传算法优化神经网络的网络流量预测模型.首先将相空间重构和神经网络参数进行编码,网络流量预测精度作为目标函数,然后通过遗传算法选择模型最优参数,最后进行网络流量仿真实验.实验结果表明相对传统预测模型,遗传优化神经网络模型具有更高预测精度及稳定性更好.
In order to improve the network traffic prediction accuracy, this paper proposes a network traffic prediction method based on RBF neural network optimized by genetic algorithm which uses the relation between phase space reconstruction and parameters of prediction model. Firstly, phase space reconstruction and the parameters of RBF neural network are coded, and then the model prediction accuracy is used as the objection function, and optimal parameters of the model are selected by genetic algorithm, lastly, the simulation experiments are carried out to test model＇s performance. The results show that, compared with the traditional models, the proposed model improves the prediction accuracy.
出处
《微电子学与计算机》
CSCD
北大核心
2013年第3期132-135,共4页
Microelectronics & Computer
关键词
网络流量
神经网络
遗传算法
相空间重构
network traffic
neural network
genetic algorithm
phase space reconstruction
