Abstract
To cope up with the variability of defect shadows and the complexity between defect characters and classes in welding image and poor generalization of fuzzy neural network (FNN), a support vector machine (SVM)-based FNN classification algorithm for welding defect is presented. The algorithm firstly adopts supervisory fuzzy cluster to get the rules of input and output space and similarity probability is applied to calculate the importance of rules. Then the parameters and structure of FNN are determined through SVM. Finally, the FNN is trained to classify the welding defects. Simulation for recognizing defects in welding images shows the efficiency of the presented.
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© 2007 Springer Berlin Heidelberg
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Zhang, Xg., Ren, Sj., Zhang, Xg., Zhao, F. (2007). Fuzzy Neural Network Classification Design Using Support Vector Machine in Welding Defect. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_27
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DOI: https://doi.org/10.1007/978-3-540-72393-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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