Crack sizing for alternating current field measurement based on GRNN
Zheng Xian-bin · Zhongguo Shiyou Daxue xuebao. Ziran kexue ban · 2007
Considering the deficiency of the precision and intelligence of the crack sizing method used in the alternating current field measurement(ACFM),the generalized regression neural network(GRNN) was introduced.On the basis of the finite element simulating experiment,characteristic vectors were picked up as input-elements of GRNN.Using normalized discrete data as training and testing samples,the main information was saved in the GRNN model,and the intrinsic relationship between input and output was found out.Finally,the GRNN model was used to forecast unknown points.The results indicate that compared with the traditional linear interpolation and BP neural network,the GRNN is more precise,intelligent and generalized,and it has the strong adaptability with few training and testing samples,which guarantees the precision and generalization of the model for crack-sizing forecasting in ACFM.