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Received:June 09, 2016 Revised:August 08, 2016
Received:June 09, 2016 Revised:August 08, 2016
中文摘要: 支持向量机具有良好的非线性建模能力，其参数对网络流量预测结果有直接影响，为了解决支持向量机的参数确定的难问题，根据杂草算法的优势，提出了改进支持向量机的网络流量预测模型.首先收集大量网络数量原始数据，将支持向量机参数作为杂草种子，然后模拟杂草的生存、繁殖过程搜索最优参数寻优，建立网络流量预测模型，最后采用具体网络流量数据测试模型的可行性.结果表明，该模型不仅得到了高精度的网络流量预测结果，而且可以应用网络流量管理中.
Abstract:Abstract:Aiming at parameters optimization problem of support vector machine in network traffic predicting, a network traffic predicting model is proposed based on improved support vector machine. Parameters of support vector machine are considered as a weed, the optimal parameters are found by invasive weed optimization algorithm, and network traffic data is used to test the performance. The experimental results show that the proposed model obtains high predicting accuracy and fastens the model speed, and it can meet the requirements of network traffic predicting.
keywords: network traffic invasive weed optimization algorithm chaotic theory support vector machine
文章编号： 中图分类号： 文献标志码：
基金项目:广东省教育厅项目（2010TJK446）
| Author Name | Affiliation |
| WANG Xue-Song | Department of Electronic Information, Foshan Polytechnic College, Foshan 528137, China |
| Author Name | Affiliation |
| WANG Xue-Song | Department of Electronic Information, Foshan Polytechnic College, Foshan 528137, China |
引用文本：
王雪松.改进支持向量机的网络流量预测.计算机系统应用,2017,26(3):230-233
WANG Xue-Song.Network Traffic Predicting Model Based on Improved Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2017,26(3):230-233
王雪松.改进支持向量机的网络流量预测.计算机系统应用,2017,26(3):230-233
WANG Xue-Song.Network Traffic Predicting Model Based on Improved Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2017,26(3):230-233
