Research of neural network algorithm based on FA and RBF

This paper proposes a radial basis function (RBF) neural network algorithm based on factor analysis (FA-RBF) with the architecture feature of RBF network when the data are high-dimensional and complex. By reducing the feature dimension of the original data, FA-RBF algorithm regards the data after dimension reduction as the inputs of the RBF network, and then trains and simulates the network. The algorithm obviously simplifies the network architecture. By analyzing an example, the results show when the algorithm´s predicted precision is not reduced, the convergence velocity is improved, the running time is saved and the error of the predicted value is reduced. In order to test and verify the validity of the new algorithm, we compare it with the RBF neural network algorithm based on principal component analysis (PCA-RBF), the predicted results of FA-RBF algorithm are better than the results of RBF and PCA-RBF algorithm.