Applications of BP, Convolutional and RBF Networks

Abstract

By studying the effects of different types of feed forward neural networks in different fields, the applicable environment of different neural networks can be judged, which will make it easier for people to choose appropriate neural network when it's needed. To achieve this, in this article I summarize and classify the existing neural network experiences and feedback results, and compare the data before and after using the neural network. The data shows that BP networks can improve the resolution or accuracy of problems with no obvious influencing factors. Convolutional networks can increase the accuracy of image processing to more than 95%, and RBF networks can calculate high-precision data curves. Thus, it can be concluded that the BP network is suitable for solving problems with unclear influencing factors, the convolutional network has more image processing problems, and the RBF network has a higher frequency of use when higher results are required.