计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
6期
64-67
,共4页
网络流量预测%相空间重构%神经网络%粒子群优化算法
網絡流量預測%相空間重構%神經網絡%粒子群優化算法
망락류량예측%상공간중구%신경망락%입자군우화산법
network traffic prediction%phase space reconstruction%neural network%Particle Swarm Optimization(PSO)
为了提高网络流量预测精度，利用相空间重构和神经网络参数间的相互联系，提出一种参数联合优化的网络流量非线性预测模型。将相空间重构和预测模型参数作为粒子群优化算法的粒子，网络流量预测精度作为粒子适应度函数，通过粒子之间相互协作获得全局最优参数，根据最优参数建立最优网络流量非线性预测模型，通过网络流量实例对模型性能进行测试。结果表明，相对于传统参数优化方法，参数联合优化方法大幅度提高了网络流量的预测精度，为非线性预测问题提供了一种新的研究思路。
為瞭提高網絡流量預測精度，利用相空間重構和神經網絡參數間的相互聯繫，提齣一種參數聯閤優化的網絡流量非線性預測模型。將相空間重構和預測模型參數作為粒子群優化算法的粒子，網絡流量預測精度作為粒子適應度函數，通過粒子之間相互協作穫得全跼最優參數，根據最優參數建立最優網絡流量非線性預測模型，通過網絡流量實例對模型性能進行測試。結果錶明，相對于傳統參數優化方法，參數聯閤優化方法大幅度提高瞭網絡流量的預測精度，為非線性預測問題提供瞭一種新的研究思路。
위료제고망락류량예측정도，이용상공간중구화신경망락삼수간적상호련계，제출일충삼수연합우화적망락류량비선성예측모형。장상공간중구화예측모형삼수작위입자군우화산법적입자，망락류량예측정도작위입자괄응도함수，통과입자지간상호협작획득전국최우삼수，근거최우삼수건립최우망락류량비선성예측모형，통과망락류량실례대모형성능진행측시。결과표명，상대우전통삼수우화방법，삼수연합우화방법대폭도제고료망락류량적예측정도，위비선성예측문제제공료일충신적연구사로。
In order to improve the prediction precision of network traffic, this paper proposes a nonlinear network traffic predic-tion model based on parameters joint optimization algorithm, which uses the relationship between the phase space reconstruction and prediction model parameters. The phase space reconstruction and prediction model parameters are taken as particle of Improved Particle Swarm Optimization algorithm(IPSO)while the prediction accuracy of network traffic as the evaluation function of CPSO, and then, the optimization parameters are obtained by collaboration among particles. The optimal nonlinear network traffic prediction model is built according to the parameters. The performance of the proposed model is tested by network traffic data. The results show that the proposed method has improved the prediction precision of network traffic compared with the traditional parameters optimization algorithm;it has provided a new way for the nonlinear prediction problem.
