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https://dspace.univ-ouargla.dz/jspui/handle/123456789/3534
Title: | Autoregressive Modeling preprocessing with SVM Optimization based on BFA for Bearing Fault Diagnosis |
Authors: | T. Thelaidjia , S. Chenikher |
Keywords: | Bacterial Foraging Algorithm Autoregressive Modeling Support Vector Machine Wavelet Packet Fault Diagnosis Roller Bearing Machine Learning Time Series Analysis Vibration Measurement |
Issue Date: | 22-Dec-2013 |
Abstract: | As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, extracting feature is very important. This paper proposes the application of Autoregressive Modeling to SVM for feature extraction. According to the fact that parameter selection of support vector machine(SVM) for fault diagnosis is difficult, a new method based on bacterial foraging algorithm(BFA) for support vector machine parameter optimization was proposed , then the faster optimization of the parameters ”c” and kernel parameter ”σ” was performed. The results have shown feasibility and effectiveness of the proposed approach. |
Description: | The INTERNATIONAL CONFERENCE ON ELECTRONICS & OIL: FROM THEORY TO APPLICATIONS March 05-06, 2013, Ouargla, Algeria |
URI: | http://hdl.handle.net/123456789/3534 |
ISSN: | waf |
Appears in Collections: | 3. Faculté des sciences appliquées |
Files in This Item:
File | Description | Size | Format | |
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T_Thelaidjia.pdf | 1,61 MB | Adobe PDF | View/Open |
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