微电子学与计算机
微電子學與計算機
미전자학여계산궤
MICROELECTRONICS & COMPUTER
2013年
3期
132-135
,共4页
网络流量%神经网络%遗传算法%相空间重构
網絡流量%神經網絡%遺傳算法%相空間重構
망락류량%신경망락%유전산법%상공간중구
network traffic%neural network%genetic algorithm%phase space reconstruction
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.
為瞭提高網絡流量預測精度,利用相空間重構和預測模型參數間的相互聯繫,提齣一種遺傳算法優化神經網絡的網絡流量預測模型.首先將相空間重構和神經網絡參數進行編碼,網絡流量預測精度作為目標函數,然後通過遺傳算法選擇模型最優參數,最後進行網絡流量倣真實驗.實驗結果錶明相對傳統預測模型,遺傳優化神經網絡模型具有更高預測精度及穩定性更好.
위료제고망락류량예측정도,이용상공간중구화예측모형삼수간적상호련계,제출일충유전산법우화신경망락적망락류량예측모형.수선장상공간중구화신경망락삼수진행편마,망락류량예측정도작위목표함수,연후통과유전산법선택모형최우삼수,최후진행망락류량방진실험.실험결과표명상대전통예측모형,유전우화신경망락모형구유경고예측정도급은정성경호.
@@@@ 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 .
