Flaw Identification Based on Layered Multi-subnet Neural Networks

Pointed to the disadvantages such as low recognizing precision, long training time and limited recognizing range of single neural network in eddy current testing, layered multi-subnet neural network was presented. It was composed by a sumnet and several layered subnets, and could divide a complex task into a series of subtasks, so it could quickly identify whether the defect was existed, and also the defect location and dimension. Because of the improved RBF and wavelet multi-scaling edge detecting were used in each network, the network structure was simplified much. The result showed that layered multi-subnet neural network was suitable to online eddy current testing.