Acoustic Emission Signal Classification Based On Support Vector Machine

Yang Yu, Liang Zhou


A classification of acoustic emission signals has great significance. The best parameters of the RBF kernel had obtained by using grid optimization method, and the classifier had built to achieve the identification and classification of acoustic emission signals. The simulation results show that support vector machine can effectively distinguish different acoustic emission signal and noise signal.


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