Pattern Recognition for Printing Quality Control Chart Based on MC Method and BP Neural Network

A mathematical model for patterns of the printing quality control chart is established,and the data of printing quality is simulated based on Monte Carlo method,Then the complexity of the sample data is reduced by using the method of standard transformation and linear encoding.A 4-layer BP neural network model,as 24-18-16-4,is established through the experiments,and a scaled conjugated gradient training algorithm is adopted to enhance the stability and convergence of the network.The paper uses different capacity of training samples in pattern recognition for control chart,and the recognition accuracy achieves 95.87%.Results of experiments show that this method can improve the level of quality control and degree of automation for printing enterprise.