Network traffic nonlinear prediction combined with mutifractal
By analyzing the correlation structure of multifractal tree,it was found that multifractal has the ability to con-vert the non-stationary long-range dependence（LRD） trace to a series of short-range dependence（SRD） sequence.Based on this property,a FIR neural network traffic predictor combined with m...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2007-01-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74659790/ |
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| _version_ | 1841537419084562432 |
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| author | WANG Sheng-hui QIU Zheng-ding |
| author_facet | WANG Sheng-hui QIU Zheng-ding |
| author_sort | WANG Sheng-hui |
| collection | DOAJ |
| description | By analyzing the correlation structure of multifractal tree,it was found that multifractal has the ability to con-vert the non-stationary long-range dependence（LRD） trace to a series of short-range dependence（SRD） sequence.Based on this property,a FIR neural network traffic predictor combined with multifractal was proposed.Because the LRD fea-ture of trace is used,the multi-step performance of proposed method is much better than traditional methods. |
| format | Article |
| id | doaj-art-f67ce7a49da942799453a23e271d2cf1 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2007-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-f67ce7a49da942799453a23e271d2cf12025-01-14T08:36:43ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2007-01-01455074659790Network traffic nonlinear prediction combined with mutifractalWANG Sheng-huiQIU Zheng-dingBy analyzing the correlation structure of multifractal tree,it was found that multifractal has the ability to con-vert the non-stationary long-range dependence（LRD） trace to a series of short-range dependence（SRD） sequence.Based on this property,a FIR neural network traffic predictor combined with multifractal was proposed.Because the LRD fea-ture of trace is used,the multi-step performance of proposed method is much better than traditional methods.http://www.joconline.com.cn/zh/article/74659790/traffic modelingnetwork traffic predictionmultifractal |
| spellingShingle | WANG Sheng-hui QIU Zheng-ding Network traffic nonlinear prediction combined with mutifractal Tongxin xuebao traffic modeling network traffic prediction multifractal |
| title | Network traffic nonlinear prediction combined with mutifractal |
| title_full | Network traffic nonlinear prediction combined with mutifractal |
| title_fullStr | Network traffic nonlinear prediction combined with mutifractal |
| title_full_unstemmed | Network traffic nonlinear prediction combined with mutifractal |
| title_short | Network traffic nonlinear prediction combined with mutifractal |
| title_sort | network traffic nonlinear prediction combined with mutifractal |
| topic | traffic modeling network traffic prediction multifractal |
| url | http://www.joconline.com.cn/zh/article/74659790/ |
| work_keys_str_mv | AT wangshenghui networktrafficnonlinearpredictioncombinedwithmutifractal AT qiuzhengding networktrafficnonlinearpredictioncombinedwithmutifractal |