计算机应用与软件
計算機應用與軟件
계산궤응용여연건
COMPUTER APPLICATIONS AND SOFTWARE
2014年
10期
129-131,153
,共4页
网络流量%最小二乘支持向量机%粒子群优化算法%变异粒子%预测模型
網絡流量%最小二乘支持嚮量機%粒子群優化算法%變異粒子%預測模型
망락류량%최소이승지지향량궤%입자군우화산법%변이입자%예측모형
Traffic network%Least square support vector machine(LSSVM)%Particle swarm optimisation%Mutation particle%Prediction model
针对最小二乘支持向量机参数优化问题,提出一种变异粒子群算法优化最小二乘支持向量的网络流量预测模型(MPSO-LSSVM)。首先对网络流量序列进行相空间重构,构建最小二乘支持向量的学习样本;然后采用变异粒子群算法选择最小二乘支持向量机参数,从而建立最优的网络流量预测模型,最后与其他模型进行对比实验。对比结果表明,相对于对比模型,MPSO-LSSVM提高了网络流量的预测精度,预测结果可以为网络管理员提供有价值参考信息。
針對最小二乘支持嚮量機參數優化問題,提齣一種變異粒子群算法優化最小二乘支持嚮量的網絡流量預測模型(MPSO-LSSVM)。首先對網絡流量序列進行相空間重構,構建最小二乘支持嚮量的學習樣本;然後採用變異粒子群算法選擇最小二乘支持嚮量機參數,從而建立最優的網絡流量預測模型,最後與其他模型進行對比實驗。對比結果錶明,相對于對比模型,MPSO-LSSVM提高瞭網絡流量的預測精度,預測結果可以為網絡管理員提供有價值參攷信息。
침대최소이승지지향량궤삼수우화문제,제출일충변이입자군산법우화최소이승지지향량적망락류량예측모형(MPSO-LSSVM)。수선대망락류량서렬진행상공간중구,구건최소이승지지향량적학습양본;연후채용변이입자군산법선택최소이승지지향량궤삼수,종이건립최우적망락류량예측모형,최후여기타모형진행대비실험。대비결과표명,상대우대비모형,MPSO-LSSVM제고료망락류량적예측정도,예측결과가이위망락관리원제공유개치삼고신식。
In light of the problem of LSSVM parameters optimisation,in this paper we propose a network traffic prediction model which is based on optimising LSSVM by mutation particle swarm optimisation (MPSO-LSSVM).First,the phase space reconstruction is made on network traffic sequence to construct the learning samples of least square support vector;then the mutation particle swarm optimisation is used to select the parameters of LSSVM so as to build the optimal network traffic prediction model;finally,the contrast experiment is carried out between it and other models.Comparison results show that with respect to contrast models,the MPSO-LSSVM improves the prediction accuracyof network traffic,the predicted results can provide valuable reference information for network administrators.