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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Zhou, W., Wang, C.: Researth and Application of Automatic Recognition System to Weld Defects. Transactions of the China Welding Institution 13(1), 45–50 (1992)
da Silva, R.R., Siqueira, M.H.S., Caloba, L.P., et al.: Radiographics Pattern Recognition of Welding Defects Using Linear Classifiers. Insight 43(10), 669–674 (2001)
Ren, D., You, Z., Sun, C.: Automatic Analysis System of X-ray Weld Real-time Imaging. Transactions of the China Welding Institution 21(1), 61–63 (2000)
Setnes, M.: Supervised Fuzzy Clustering for Rule Extraction. IEEE Transactions on Fuzzy Systems 8(4), 416–424 (2000)
Nauck, D., Kruse, R.: Obtaining Interpretable Fuzzy Classification Rules from Medical Data. Artificial Intelligence 16(2), 149–169 (1999)
Lin, C.-T., Yeh, C.-M., Hsu, C.-F.: Fuzzy Neural Network Classification Design Using Support Vector Machine. In: IEEE International Symposium on Circuits and Systems, vol. 5, pp. 724–727 (2004)
Sun, Z.H.: Study on Support Vector Machine and Its Application in Control, Dissertion, Zhejiang University (2003)
The National Standard of PRC: The Ray- cameras and Quantity Class of Fusion Welding Joint. GB3323-87 (1987)
Zhang, X.G.: The Extraction and Automatic Identification of Weld Defects with X-ray Inspection. National Defence Industry Press, Beijing (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)