基于人工免疫和FARIMA模型的流量预测方法研究

发布时间：2018-05-07 13:14

本文选题：拥塞 + 预测　；参考：《四川大学学报(自然科学版)》2013年02期

【摘要】：针对Internet迅速发展而造成的网络拥塞现象,基于人工免疫算法与FARIMA模型提出了一种新的流量预测方法PAIF(Prediction method based on Artificial Immune and FA-RIMA).该方法首先利用人工免疫算法建立了预测策略,并结合FARIMA模型预测结果进行融合,以此提高预测精度.其次,以实际数据进行仿真实验,深入研究了影响PAIF预测误差的因素,同时对比分析了单独使用FARIMA模型的情况.实验结果表明,PPAIF具有较好的适应性.
[Abstract]:In view of the network congestion caused by the rapid development of Internet, a new traffic prediction method, PAIF(Prediction method based on Artificial Immune and FA-RIMA, is proposed based on artificial immune algorithm and FARIMA model. In this method, the prediction strategy is established by artificial immune algorithm and combined with the prediction results of FARIMA model to improve the prediction accuracy. Secondly, the factors that affect the prediction error of PAIF are deeply studied by the simulation experiment with the actual data, and the case of using the FARIMA model alone is compared and analyzed at the same time. The experimental results show that PPAIF has good adaptability.
【作者单位】：广东石油化工学院计算机与电子信息学院;
【基金】：国家自然科学基金(61272382) 广东省科技计划项目(2012B010100037) 广东省自然科学基金(10252500002000001,S2012010009963)
【分类号】：TP393.06

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