Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network
Accurately identifying the key nodes of aviation network through technical means is of important theoretical significance and reference value for the normal operation of aviation network in peacetime and defense and repair in wartime. For this, a key node identification method based on kernel extrem...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Advances in Aeronautical Science and Engineering
2021-02-01
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| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020034?st=article_issue |
| _version_ | 1811167095998644224 |
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| author | NIU Junfeng GAN Xusheng SUN Jingjuan TU Congliang |
| author_facet | NIU Junfeng GAN Xusheng SUN Jingjuan TU Congliang |
| author_sort | NIU Junfeng |
| collection | DOAJ |
| description | Accurately identifying the key nodes of aviation network through technical means is of important theoretical significance and reference value for the normal operation of aviation network in peacetime and defense and repair in wartime. For this, a key node identification method based on kernel extreme learning machine is proposed. Firstly, the comprehensive importance of nodes based on analytic hierarchy process (AHP) is evaluated. Then, three simple indices are selected and the importance evaluation model is established based on the mapping relationship between simple indices and comprehensive importance of kernel extreme learning machine. Finally, the simulation is carried out with the example of China-US air network. The simulation analysis of the American aviation network indicates that it can obtain satisfactory identification effect for key nodes by only calculating the complex index value of 40 nodes, which reduces the calculation complexity and improves the identification efficiency. This shows that it is effective and feasible to use this method to identify the key nodes of aviation network. |
| first_indexed | 2024-04-10T16:03:23Z |
| format | Article |
| id | doaj.art-2fea12be181546a5886f094c6e1eaba8 |
| institution | Directory Open Access Journal |
| issn | 1674-8190 |
| language | zho |
| last_indexed | 2024-04-10T16:03:23Z |
| publishDate | 2021-02-01 |
| publisher | Editorial Department of Advances in Aeronautical Science and Engineering |
| record_format | Article |
| series | Hangkong gongcheng jinzhan |
| spelling | doaj.art-2fea12be181546a5886f094c6e1eaba82023-02-10T07:10:47ZzhoEditorial Department of Advances in Aeronautical Science and EngineeringHangkong gongcheng jinzhan1674-81902021-02-01121394710.16615/j.cnki.1674-8190.2021.01.00520210105Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation NetworkNIU Junfeng0GAN Xusheng1SUN Jingjuan2TU Congliang3Department of Management Technology, Xijing University, Xi'an 710123, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaCollege of Air Traffic Control and Navigation, Air Force Engineering University, Xi'an 710051, ChinaAccurately identifying the key nodes of aviation network through technical means is of important theoretical significance and reference value for the normal operation of aviation network in peacetime and defense and repair in wartime. For this, a key node identification method based on kernel extreme learning machine is proposed. Firstly, the comprehensive importance of nodes based on analytic hierarchy process (AHP) is evaluated. Then, three simple indices are selected and the importance evaluation model is established based on the mapping relationship between simple indices and comprehensive importance of kernel extreme learning machine. Finally, the simulation is carried out with the example of China-US air network. The simulation analysis of the American aviation network indicates that it can obtain satisfactory identification effect for key nodes by only calculating the complex index value of 40 nodes, which reduces the calculation complexity and improves the identification efficiency. This shows that it is effective and feasible to use this method to identify the key nodes of aviation network.http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020034?st=article_issueaviation networkkey node identificationextreme learning machineparameter optimizationkernel function |
| spellingShingle | NIU Junfeng GAN Xusheng SUN Jingjuan TU Congliang Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network Hangkong gongcheng jinzhan aviation network key node identification extreme learning machine parameter optimization kernel function |
| title | Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network |
| title_full | Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network |
| title_fullStr | Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network |
| title_full_unstemmed | Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network |
| title_short | Research on Kernel Extreme Learning Machine Algorithm for Key Node Identification in Aviation Network |
| title_sort | research on kernel extreme learning machine algorithm for key node identification in aviation network |
| topic | aviation network key node identification extreme learning machine parameter optimization kernel function |
| url | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2020034?st=article_issue |
| work_keys_str_mv | AT niujunfeng researchonkernelextremelearningmachinealgorithmforkeynodeidentificationinaviationnetwork AT ganxusheng researchonkernelextremelearningmachinealgorithmforkeynodeidentificationinaviationnetwork AT sunjingjuan researchonkernelextremelearningmachinealgorithmforkeynodeidentificationinaviationnetwork AT tucongliang researchonkernelextremelearningmachinealgorithmforkeynodeidentificationinaviationnetwork |