Improved Methods of BP Neural Network Algorithm and its Limitation

BP algorithm is a very important and classic learning algorithm. It have a wide range of applications in pattern recognition, image processing and analysis and control areas. However, in practice, we found that BP algorithm still have inadequates, such as the algorithm´s convergence is slowly, easy to converge to local minimum points, but not the overall optimal, Multiple iterations, numerical stability is poor. To solve these problems, many scholars have proposed a number of improved algorithm, this article summarizes some of the improved algorithm frequently used: Learning rate adjustment. Adding momentum term. Optimize the initialization of the right value. These three methods can improve the convergence speed and error precision of BP network. Meanwhile they can improve convergence performance and reduce the possibility of the network into a local minimum and oscillation.