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...
| Published in: | Tongxin xuebao |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | Chinese |
| Published: |
Editorial Department of Journal on Communications
2007-01-01
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| Subjects: | |
| Online Access: | http://www.joconline.com.cn/thesisDetails?columnId=74659790&Fpath=home&index=0 |
| _version_ | 1850300714886103040 |
|---|---|
| author | WANG Sheng-hui QIU Zheng-ding |
| author_facet | WANG Sheng-hui QIU Zheng-ding |
| author_sort | WANG Sheng-hui |
| collection | DOAJ |
| container_title | Tongxin xuebao |
| 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-e7985dc804bc4beca4965727a4efbb93 |
| institution | Directory of Open Access Journals |
| issn | 1000-436X |
| language | zho |
| publishDate | 2007-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| spelling | doaj-art-e7985dc804bc4beca4965727a4efbb932025-08-19T23:31:14ZzhoEditorial 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/thesisDetails?columnId=74659790&Fpath=home&index=0traffic modeling;network traffic prediction;multifractal |
| spellingShingle | WANG Sheng-hui QIU Zheng-ding Network traffic nonlinear prediction combined with mutifractal 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/thesisDetails?columnId=74659790&Fpath=home&index=0 |
| work_keys_str_mv | AT wangshenghui networktrafficnonlinearpredictioncombinedwithmutifractal AT qiuzhengding networktrafficnonlinearpredictioncombinedwithmutifractal |