Fuzzy Neural Network Classification Design Using Support Vector Machine in Welding Defect

  • Authors:

  • Xiao-Guang Zhang;Shi-Jin Ren;Xing-Gan Zhang;Fan Zhao

  • Affiliations:

  • College of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221008, and Department of Electronic Science & Engineering, Nanjing university, Nanjing, 210093 ...;Computer Science & Technology college, Xuzhou normal university, Xuzhou, 221116,;Department of Electronic Science & Engineering, Nanjing university, Nanjing, 210093, China;College of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221008,

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.