Deep neural network with RBF and sparse auto-encoders for numeral recognition

In this paper we proposed a new deep neural network architecture which is composed from a radial basis function neural network (RBF NN) followed by two auto-encoders and softmax classifier and we presented some comparison between this architecture and other architecture on numeral recognition applications. We gave also a review about RBF and sparse auto-encoder neural networks in the literature. First we defined neural networks and their different type's especially radial basis function neural networks (RBF NN) due to their specificity. Second we focused on auto-encoders and sparse coding then we moved to sparse auto-encoders and finally we demonstrated the effectiveness of our deep architecture by showing our experimental results and some comparisons.