An FCM-RSVM Algorithm Based on Feature Aggregation Degree and Its Application in Artificial Joints Defect Recognition

In order to improve the defect recognition of manual solder joints, this paper proposes a feature-aggregation-degree based combination algorithm of fuzzy C-means clustering(FCM) and relaxed support vector machine(RSVM). Firstly, the characteristics of samples are extracted based on FCM algorithm and the feature aggregation degrees are calculated according to the different memberships. Then, the slack variable parameter of RSVM algorithm is repaired based on the feature aggregation degree such that the final classification model is established. The experiment results show that the proposed algorithm can effectively reduce the effect of noise or blur point on the classification model and build a stronger generalization classification model to improve the accuracy of defect recognition.