OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS
Aiming at the fact that the classification performance of support vector machine（ SVM） highly depends on the parameters selection,a parameters optimize method of SVM based on improved fruit fly optimization algorithm（ LFOA） was proposed. The steps of SVM parameters optimize based on LFOA was propose...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2017-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.008 |
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| _version_ | 1841535147680202752 |
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| author | SUN YaoQin |
| author_facet | SUN YaoQin |
| author_sort | SUN YaoQin |
| collection | DOAJ |
| description | Aiming at the fact that the classification performance of support vector machine（ SVM） highly depends on the parameters selection,a parameters optimize method of SVM based on improved fruit fly optimization algorithm（ LFOA） was proposed. The steps of SVM parameters optimize based on LFOA was proposed,and the superiority of the algorithm in convergence speed and convergence accuracy when compared with some other methods is verified by simulation experiment of several standard datasets. Take the rolling bearing as experiment object,the common faults was diagnosed by LFOA-SVM The experiment results show that the LFOA improved the classification performance of SVM and has higher accuracy compared with FOA,GA and PSO,and can applied to fault diagnosis efficiently. |
| format | Article |
| id | doaj-art-ad8032c2ef9344ca8ce57fac7913773f |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2017-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-ad8032c2ef9344ca8ce57fac7913773f2025-01-15T02:34:45ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692017-01-013928529030598175OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSISSUN YaoQinAiming at the fact that the classification performance of support vector machine（ SVM） highly depends on the parameters selection,a parameters optimize method of SVM based on improved fruit fly optimization algorithm（ LFOA） was proposed. The steps of SVM parameters optimize based on LFOA was proposed,and the superiority of the algorithm in convergence speed and convergence accuracy when compared with some other methods is verified by simulation experiment of several standard datasets. Take the rolling bearing as experiment object,the common faults was diagnosed by LFOA-SVM The experiment results show that the LFOA improved the classification performance of SVM and has higher accuracy compared with FOA,GA and PSO,and can applied to fault diagnosis efficiently.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.008Fruit fly optimization algorithmSupport vector machineParameters optimizationFault diagnosis |
| spellingShingle | SUN YaoQin OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS Jixie qiangdu Fruit fly optimization algorithm Support vector machine Parameters optimization Fault diagnosis |
| title | OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS |
| title_full | OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS |
| title_fullStr | OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS |
| title_full_unstemmed | OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS |
| title_short | OPTIMIZED SVM BASED ON IMPROVED FOA AND ITS APPLICATION IN FAULT DAIGNOSIS |
| title_sort | optimized svm based on improved foa and its application in fault daignosis |
| topic | Fruit fly optimization algorithm Support vector machine Parameters optimization Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.008 |
| work_keys_str_mv | AT sunyaoqin optimizedsvmbasedonimprovedfoaanditsapplicationinfaultdaignosis |