关键词: BP神经网络, 裂缝分类, 复合误差函数, 分层动态调整

Abstract: Traditional BP neural network has several shortcomings that training speed is slow, probability of error is great and it is easily falling into local minimum. A kind of improved composite error function, which can replace the traditional global squared mean error function, is presented. This novel function can improve the learning rate of the network, and a new BP neural network is introduced which can adjust the learning rate hierarchically and dynamically to classify the cracks in road surface pictures. The experimental results show that, compared with traditional methods, the improved algorithm has achieved a dramatic improvement in precision and speed.

Key words: BP neural network, crack classification, composite error function, stratified dynamic adjustment