网络流量时间序列预测的ANFIS方法

S. Chabaa, A. Zeroual, J. Antari
{"title":"网络流量时间序列预测的ANFIS方法","authors":"S. Chabaa, A. Zeroual, J. Antari","doi":"10.1109/MMS.2009.5409834","DOIUrl":null,"url":null,"abstract":"In This paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision in the prediction process of internet traffic in terms of statistical indicators. This model fits well real data and provides an effective description of network condition at different times.","PeriodicalId":300247,"journal":{"name":"2009 Mediterrannean Microwave Symposium (MMS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"ANFIS method for forecasting internet traffic time series\",\"authors\":\"S. Chabaa, A. Zeroual, J. Antari\",\"doi\":\"10.1109/MMS.2009.5409834\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In This paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision in the prediction process of internet traffic in terms of statistical indicators. This model fits well real data and provides an effective description of network condition at different times.\",\"PeriodicalId\":300247,\"journal\":{\"name\":\"2009 Mediterrannean Microwave Symposium (MMS)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Mediterrannean Microwave Symposium (MMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMS.2009.5409834\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Mediterrannean Microwave Symposium (MMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMS.2009.5409834","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

摘要

本文采用模糊系统与神经网络相结合的自适应神经模糊推理系统(ANFIS)对互联网流量时间序列的输入输出数据进行预测。应用了几个统计标准来证明该模型的有效性。结果表明,从统计指标上看,ANFIS模型在互联网流量预测过程中具有较好的精度。该模型能很好地拟合实际数据,并能有效地描述不同时刻的网络状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANFIS method for forecasting internet traffic time series
In This paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of internet traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision in the prediction process of internet traffic in terms of statistical indicators. This model fits well real data and provides an effective description of network condition at different times.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信