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| Defect Recognition of Metal Magnetic Memory Testing Based on Improved Fuzzy Support Vector Machine |
| ZHU Hong-yun,WANG Chang-long,WANG Jian-bin,LIU Bing |
| Department of Electrical Engineering, Ordnance Engineering College, Shijiazhuang, Hebei 050003, China |
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Abstract Since the metal magnetic memory (MMM) signal is weak and the defects can’t be recognized effectively, a novel fuzzy support vector machine(FSVM) is proposed. In order to reduce the influence of isolation point and noise on classification accuracy, the?k?nearest neighbor dispersion is constructed based on the traditional determination method of the fuzzy membership. Besides, the feature weighted degree of each feature is calculated to reduce the influence of redundant and weak features on classification accuracy. And then the proposed approach is applied to recognize MMM signals of different areas, the experimental results show that the proposed approach can recognize this MMM signals effectively，it is more robust and has the better performance of recognition. The proposed FSVM approach is a feasible recognition algorithm for MMM signals of different areas.
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2013, Vol. 34 
