Research on Optimization and Implementation of BP Neural Network Algorithm

In recent years, BP neural network has been widely used in various fields, such as language comprehension, recognition and automatic control, etc. It has the advantages of approximating any nonlinear mapping relationship, better generalization ability, better fault tolerance, simple and easy to be implemented. This paper firstly introduces the basic principles of BP neural network from the two main processes: information forward propagation and error back propagation. Then the optimization approaches in network structure and network algorithm are presented. Finally, we implement an actual example of sine sample data with neural network tool box in MATLAB, which shows a good simulation performance in implementation.