Double-layer BP Neural Network Flatness Forecast Based on Parameter Adjustment of Sigmoid Transfer Function

In order to build a double hidden-layer BP neural network model to adjust the shape of Sigmoid activation function,a method improving the BP neural network to preprocess the plate defective data was proposed.Comparing the data of this method with the formula Levenberg-Marquardt preprocessing method,the results show the time of learning BP neural network can be effectively reduced and the network's generalization ability be improved by this method,and it benefits the on-line identification of the plate defects.