An RBF Network Approach to Flatness Pattern Recognition Based on SVM Learning

In the traditional method of flatness pattern recognition known as neural network with a changing topological configuration, slow convergence and local minimum were observed. Moreover, the process of experimenting the initial parameters and structure of the neural network according to the experience before has been proved time-consuming and complex. In this paper, a new approach was proposed based on the structural equivalence of radial basis function (RBF) network and support vector machines (SVM). The SMO algorithm was employed to obtain more optimal structure and initial parameters of RBF network, and then the BP algorithm was used to adjust RBF network slightly. The new approach with the advantages of SVM, such as fast learning and whole optimization, was efficient and intelligent