Research and application of integration of RS and FNN in defect recognition of welding.

Because the detect recognition characteristic extracted from the welding image bas been seriously interfered by noises, and the accuracy of existing recognition algorithms is low, a defect recognition algorithm integrating rough set (RS) with fuzzy neural network (FNN) is presented in this paper. Firstly, the fuzzy C-mean (FCM) clustering algorithm was adopted to discretize the attributes of samples, and RS was used to reduce the attributes of sample data and obtain decision rules, then π function was used to fuzzified the attributes of samples according to the center and radius of clustering to overcome the problem that RS is sensitive to noises. Then, the obtained reduced fuzzy decision rules and fuzzy logical inference were used to ascertain the structure of FNN, and dependent factors together with antecedent coverage factors were employed to determine the initial parameters of network. In consideration of the reliability of the data in the samples, the weighted cost function was used to adjust model parameters. The simulation result shows that this algorithm can solve the problems, such as the uncertainty of sample data caused by noise interference in the process of classification and the difficulty in determining the structure of FNN, and can greatly improve the recognition capability of welding image defects. [ABSTRACT FROM AUTHOR]