遗传算法优化小波神经网络的网络流量预测

为了提高网络流量的预测精度,克服小波神经网络收敛速度慢、易陷入局部最优的缺点,提出一种遗传算法优化小波神经网络的网络流量预测模型.首先计算延迟时间和嵌入维数,构建小波神经网络的学习样本,然后采用小波神经网络对网络流训练集进行学习,并采用改进遗传算法对小波神经网络参数进行全局寻优,提高收敛速度和网络学习精度,最后采用网络流量数据对模型性能进行仿真分析.结果表明,相对于对比模型,本文模型的平均误差大幅度降低,训练次数急剧减,减小了二次优化训练的次数,具有更大的实际应用价值.

Network Traffic Prediction Based on Wavelet Neural Network and Genetic Algorithm

In order to overcome the shortcomings of wavelet neural network and improve prediction precision of network traffic, a novel network traffic prediction model is proposed based on the wavelet neural network and genetic algorithm in this paper. Firstly, the time delay and embedding dimension of network traffic are calculated to construct the learning samples of wavelet neural network. Then training samples are input to wavelet neural network to learn in which improved genetic algorithm is used to optimize the parameters of wavelet neural network. Finally, the performance of model is tested by simulation experiment using network traffic data. The results show that the proposed model has reduced the prediction error and the number of training has reduced sharply compared with other model, so it has great practical application value.