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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| Main Author: | |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2017-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2017.02.008 |
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| Summary: | 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. |
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| ISSN: | 1001-9669 |