基于PSO-Elman模型的网络流量预测
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摘要
针对网络流量的混沌性特点以及传统神经网络处理网络流量预测问题易陷入局部极小导致预测精度不高的问题,提出在相空间重构基础上,采用粒子群算法(PSO)优化Elman神经网络初始参数的网络流量预测模型。首先对网络流量时间序列进行相空间重构,将重构后的流量序列作为模型的输入;再利用PSO算法全局搜索能力对Elman神经网络初始参数进行优化;最后利用训练好的Elman神经网络对网络流量进行预测。仿真结果表明,相比其他流量预测方法,基于PSOElman模型的网络流量预测提高了预测准确率。
The traditional neural network used for network traffic prediction is easy to fall into the local minimization,which may lead to the low prediction accuracy. Therefore,a network traffic prediction model is proposed on the basis of phasespace reconstruction,in which the particle swarm optimization(PSO) algorithm is used to optimize the initial parameter of Elman neural network. The phase-space reconstruction is carried out for the time sequence of network traffic,and then the reconstructed traffic sequence is taken as the input of the model. The global searching ability of PSO algorithm is utilized to optimize the initial parameter of Elman neural network. The trained Elman neural network is used to forecast the network traffic.The simulation results show that,in comparison with other traffic prediction models,the network prediction based on PSO-Elman model has higher prediction accuracy.
The traditional neural network used for network traffic prediction is easy to fall into the local minimization,which may lead to the low prediction accuracy. Therefore,a network traffic prediction model is proposed on the basis of phasespace reconstruction,in which the particle swarm optimization(PSO) algorithm is used to optimize the initial parameter of Elman neural network. The phase-space reconstruction is carried out for the time sequence of network traffic,and then the reconstructed traffic sequence is taken as the input of the model. The global searching ability of PSO algorithm is utilized to optimize the initial parameter of Elman neural network. The trained Elman neural network is used to forecast the network traffic.The simulation results show that,in comparison with other traffic prediction models,the network prediction based on PSO-Elman model has higher prediction accuracy.
引文
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[3]孙轶轩,邵春福,计寻,等.基于ARIMA与信息粒化SVR组合模型的交通事故时序预测[J].清华大学学报(自然科学版),2014,54(3):348-353.SUN Yixuan,SHAO Chunfu,JI Xun,et al. Urban traffic accident time series prediction model based on combination of ARIMA and information granulation SVR[J]. Journal of Tsinghua University(natural science),2014,54(3):348-353.
[4]陈珂,彭志平,柯文德.基于人工免疫和FARIMA模型的流量预测方法研究[J].四川大学学报(自然科学版),2013,50(2):267-272.CHEN Ke,PENG Zhiping,KE Wende. The study of traffic prediction method based on artificial immune and FARIMA model[J]. Journal of Sichuan University(natural science edition),2013,50(2):267-272.
[5]王相守,张娜.基于灰色神经网络组合模型的光伏短期出力预测[J].电力系统自动化,2012,36(19):37-41.WANG Xiangshou,ZHANG Na. Short-term output power forecast of photovoltaic based on a grey and neural network hybrid model[J]. Automation of electric power systems, 2012, 36(19):37-41.
[6]唐舟进,彭涛,王文博.一种基于相关分析的局域最小二乘支持向量机小尺度网络流量预测算法[J].物理学报,2014,63(13):1-10.TANG Zhoujin, PENG Tao, WANG Wenbo. A local least square support vector machine prediction algorithm of small scale network traffic based on correlation analysis[J]. Acta physica Sinica,2014,63(13):1-10.
[7]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014,35(11):2423-2432.LI Zhinong,ZHU Ming,CHU Fulei,et al. Mechanical fault diagnosis method based on empirical wavelet transform[J]. Chinese journal of scientific instrument,2014,35(11):2423-2432.
[8]郭通,兰巨龙,李玉峰,等.基于量子自适应粒子群优化径向基函数神经网络的网络流量预测[J].电子与信息学报,2013,35(9):2220-2226.GUO Tong,LAN Julong,LI Yufeng,et al. Network traffic prediction with radial basis function neural network based on quantum adaptive particle swarm optimization[J]. Journal of electronics&information technology,2013,35(9):2220-2226.
[9]李翔,朱全银.Adaboost算法改进BP神经网络预测研究神经网络预测研究[J].计算机工程与科学,2013,35(8):96-102.LI Xiang,ZHU Quanyin. Prediction of improved BP neural network by Adaboost algorithm[J]. Computer engineering&science,2013,35(8):96-102.
[10]张立仿,张喜平.量子遗传算法优化BP神经网络的网络流量预测[J].计算机工程与科学,2016,38(1):114-119.ZHANG Lifang,ZHANG Xiping. Network traffic prediction based on BP neural networks optimized by quantum genetic algorithm[J]. Computer engineering&science,2016,38(1):114-119.
[11] LU Jinjun,WANG Zhiquan. Internet traffic data flow forecast by RFB neural network based on phase space reconstruction[J]. Transactions of Nanjing University of Aeronautics&Astronutics,2006,23(4):52-63.
[12] Bellcore Lab. Traces available in the internet traffic archive[EB/OL].[2007-03-24]. http://ita.ee.lbl.gov/html/traces.html.
[13]姜明,吴春明,张旻,等.网络流量预测中的时间序列模型比较研究[J].电子学报,2009,37(11):2353-2358.JIANG Ming,WU Chunming,ZHANG Min,et al. Research on the comparison of time series models for network traffic prediction[J]. Acta electronica Sinica,2009,37(11):2353-2358.
[14] VAN DEN BERGH F. An analysis of particle swarm optimizers[D]. South Africa:University of Pretoria,2001.